Drug safety; science relating to adverse effects of pharmaceutical products
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45 episodes with Pharmacovigilance
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3 episodes with Pharmacovigilance
2 episodes with Pharmacovigilance
2 episodes with Pharmacovigilance
3 episodes with Pharmacovigilance
Dans cet épisode de Puls'In Vet, nous parlons du Librela, traitement anti-NGF qui a conquis les cabinets vétérinaires pour sa simplicité d'usage et sa promesse de soulager la douleur chronique.
Send us a textDr. Mirza Rahman, MD, MPH serves as the President of the American College of Preventive Medicine ( ACPM - https://www.acpm.org/about-acpm/governance/executive-officers/mirza-rahman/ ), a professional community, founded in 1954 as a professional community for board-certified physicians to network, share their expertise and advocate for the advancement of prevention. Today, ACPM represents over 2,000 physicians, medical students, non-physicians, and other partners. All believe in the importance of preventive medicine in our society. Dr. Rahman also serves as the Senior Vice President, Patient Safety & Pharmacovigilance at Cybin ( https://cybin.com/our-team/ ), a pharmaceutical company seeking to bring novel, second-generation psychedelics to market. He also and is a Co-Founder and President of the Guyanese Diaspora Charity ( https://www.guyanesediasporacharity.org/ ), a 501(c)(3) non-profit organization focused on helping to improve the lives of Guyanese. In addition, Dr. Rahman is an Adjunct Associate Professor of Epidemiology at Columbia University ( https://www.publichealth.columbia.edu/profile/mirza-i-rahman-md ) and as an Adjunct Associate Professor at the University of Guyana.Most recently, Dr. Rahman was the Vice President & Chief Safety Officer at Organon, a global healthcare company. There, he was responsible for leading and setting the broad strategic direction for pharmacovigilance for this multinational company.Prior to that, Dr. Rahman was the Senior Vice President, Chief Global Pharmacovigilance Officer & European Research & Development Lead at Otsuka Pharmaceuticals, a global Japanese based pharmaceutical company. In 2013, he created the ACPM - Otsuka Pharmacovigilance Physician Program in Global Medical Safety. Dr. Rahman joined Otsuka from Merck Research Laboratories, where he was an Executive Director in the Clinical Risk Management/Global Safety department. Before this, he worked at Johnson & Johnson in a variety of positions, serving as a Worldwide Vice President, Health Economics & Reimbursement at Ortho-Clinical Diagnostics in his last role there.During his 25+ years in the pharmaceutical industry, while Dr. Rahman has worked primarily in Pharmacovigilance, he has also worked in Medical Affairs, Medical Information, Health Economics and Outcomes Research, Quality Management, Clinical Development, Manufacturing, and Regulatory Affairs.Dr. Rahman completed his Public Health & General Preventive Medicine Residency along with his Family Medicine Residency at Stony Brook University. He completed the Advanced Management Program at the Columbia Business School and earned his Master of Public Health degree from the Columbia University School of Public Health. Dr. Rahman earned his Doctor of Medicine degree from the Stony Brook University School of Medicine, and his Bachelor of Science degree from the Sophie Davis School of Biomedical Education at the City College of the City University of New York. #MirzaRahman #AmericanCollegeOfPreventiveMedicine #PatientSafety #Pharmacovigilance #GuyaneseDiasporaCharity #Epidemiology #ColumbiaUniversity #UniversityOfGuyana #MedicalAffairs #HealthEconomics #OutcomesResearch #QualityManagement #ClinicalDevelopment #RegulatoryAffairs #PublicHealth #BrainHealth #DiabetesPrevention #ReducingHypertension #LifestyleMedicine #PopulationHealth #ViolencePrevention #Psychedelics #ProgressPotentialAndPossibilities #IraPastor #Podcast #Podcaster #Podcasting #ViralPodcast #STEM #Innovation #Science #Technology #ResearchSupport the show
We're excited to welcome Oeystein Kjoersvik to AI Uncovered. Oeystein leads the Generative AI program within the Quality Assurance team at Merck, where he focuses on developing AI tools and applying a quality-first approach to ensure safe, effective use of AI in GxP-regulated environments.In this episode, Tim and Oeystein delve into the challenges of adopting AI in regulated settings, the complexity of validating GenAI tools, and the transition from traditional processes to AI-augmented systems. They also explore Oeystein's work with the IMPALA Consortium and the importance of cross-industry collaboration to identify and scale high-value use cases for Generative AI.Before his current role, Oeystein served as a Product Owner in Analytics at Merck IT, building analytics platforms and integrating data science across systems. He also contributed as a Machine Learning Subject Matter Expert to TransCelerate's Intelligent Automation Group, advancing AI applications in pharmacovigilance.Oeystein brings a rare blend of technical expertise and regulatory insight. He's passionate about helping teams adopt AI responsibly and transparently—ensuring innovation aligns with quality across the pharmaceutical landscape.Welcome to AI Uncovered, a podcast for technology enthusiasts that explores the intersection of generative AI, machine learning, and innovation across regulated industries. With the AI software market projected to reach $14 trillion by 2030, each episode features compelling conversations with an innovator exploring the impact of generative AI, LLMs, and other rapidly evolving technologies across their organization. Hosted by Executive VP of Product at Yseop, Tim Martin leads a global team and uses his expertise to manage the wonderful world of product.
Pharmacovigilance is a big work that we're going to break down in this episode, to look at the data around safety events, or patient experienced side effects in clinical trials and after a drug is on the market. Where does the data come from, and what is done with it. We are going to dive into the WHO definition of pharmacovigilance, and look out for a special appearance from our favourite shiny rock! Remember, you can get in touch with us via clinical.research.intro@gmail.com. Please feel free to send questions, comments and compliments for Elyse to read out on the pod. It's fun to make Debbie squirm! Credit to our friend Sam Winnie for their awesome and cute music. Check out their work at https://www.samwinnie.com/
In this powerful episode of This Week in Pharmacy, TWIRx we're spotlighting the critical leadership roles pharmacists are taking in two vital areas of patient care: pharmacovigilance and hormone treatment therapies.
There are many reasons why use of medical products during pregnancy requires special attention. First and foremost, we want to be sure that the medicine is as safe as possible for both the pregnant person and the unborn child. Unfortunately, the safety profiles of medicines used in pregnancy are often incomplete, which makes it difficult for patients and healthcare professionals to make informed decisions.The Research section at Uppsala Monitoring Centre has a team that is currently focussing their efforts on pregnancy-related pharmacovigilance (PV). In this episode, Data scientists Sara Vidlin and Levente Papai, and Senior Pharmacovigilance scientist Lovisa Sandberg from this team, discuss complexities and challenges of pregnancy-related PV, and new solutions for addressing those challenges. Tune in to find outWhy is the world still behind when it comes to pregnancy-related pharmacovigilance?What are the challenges faced by pharmacovigilance assessors wanting to look at pregnancy cases?How can healthcare professionals, patients and carers help assessors overcome these challenges, when reporting pregnancy-related adverse drug events?How can the VigiBase pregnancy algorithm, and other algorithms, support the identification of pregnancy cases?How to use the VigiBase pregnancy algorithmUsers of VigiLyze and VigiBase Custom Searches can use the VigiBase pregnancy algorithm as a filter when performing searches. In the qualitative view in VigiLyze, click on “Filter” -> “Patient” -> “Pregnancy” to apply the filter.Want to know more?Read about the VigiBase pregnancy algorithm in this Uppsala Reports article and in this poster, presented at the International Society for Pharmaceutical Engineering (ISPE) 2024 annual meeting. EURAP – an international prospective observational study of pregnancies with antiepileptic drugs. EURAP - International Registry of Antiepileptic Drugs and PregnancyThe International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) - E2B(R3) Individual Case Safety Report (ICSR) Specification and Related Files: ich.org/page/e2br3-individual-case-safety-report-icsr-specification-and-related-filesThe concePTION project: https://www.imi-conception.eu/Medical Dictionary for Regulatory Activities (MedDRA®) support documentation: https://www.meddra.org/how-to-use/support-documentation/englishZaccaria C, Piccolo L, Gordillo-Marañón M, et al. Identification of Pregnancy Adverse Drug Reactions in Pharmacovigilance Reporting Systems: A Novel Algorithm Developed in EudraVigilance. Drug Saf. https://doi.org/10.100Join the conversation on social mediaFollow us on X, LinkedIn, or Facebook and share your thoughts about the show with the hashtag #DrugSafetyMatters.Got a story to share?We're always looking for new content and interesting people to interview. If you have a great idea for a show, get in touch!About UMCRead more about Uppsala Monitoring Centre and how we work to advance medicines safety.
David Liew talks to Claire Larter, a medical officer at the pharmacovigilance branch of the TGA, about her paper on adverse event reporting. Claire explains why pharmacovigilance is important for clinicians, and outlines what type of adverse events should be reported. The conversation also covers the process of reporting adverse events and how the TGA handles reports. Read the full article by Claire and her co-authors in Australian Prescriber.
Human and veterinary pharmacovigilance (PV) share many goals, challenges and approaches. But there are also significant differences, such as the numerous animal species that veterinary PV needs to take into account. In this two-part episode of Drug Safety Matters, James Mount, Veterinary Pharmacovigilance assessor at the Swedish Medical Products Agency, and EU elected chair of the Pharmacovigilance Working Party for veterinary medicinal products, joins the show to talk about veterinary PV practice and its differences and similarities to human PV. In part 2, you will hear aboutdifferences in types of ADRs reported for animals as compared to humans,when humans are accidentally exposed to medicines for animals, and vice versa,how the many species and breeds included in veterinary PV affects the coding of ADR reports, breed-specific ADRs – what is safe for one breed of e.g. dog or pig, may not be appropriate for another, the EU Veterinary Big Data Strategy,... and much more! Links for further readingThe public portal of the European Union Veterinary Pharmacovigilance Database.One Health – an integrated approach to the well-being of people, animals and the environment.A survey of veterinary professionals in Sweden, about practices and attitudes in relation to ADR reporting.A review of adverse events in animals and children after secondary exposure to transdermal hormone-containing medicines. The EMA Big Data strategy for veterinary medicines. Data quality framework for medicines regulation | European Medicines Agency (EMA)Small Animal Veterinary Surveillance Network (SAVSNET) - University of LiverpoolVetCompass - Royal Veterinary College, RVCReflection paper on the use of artificial intelligence in the lifecycle of medicines | European Medicines Agency (EMA)Veterinary good pharmacovigilance practices (VGVP) | European Medicines Agency (EMA)Veterinary Dictionary for Drug Regulatory Activities (VeDDRA) | European Medicines Agency (EMA) Join the conversation on social mediaFollow us on X, LinkedIn, or Facebook and share your thoughts about the show with the hashtag #DrugSafetyMatters.Got a story to share?We're always looking for new content and interesting people to interview. If you have a great idea for a show, get in touch!About UMCRead more about Uppsala Monitoring Centre and how we work to advance medicines safety.
Clinical research is undergoing a revolution in light of new demands for speed and opportunities from a technological standpoint. These trends have given rise to a debate about the quality and clinical meaning of traditional methods of investigations versus modern types of clinical studies to collect real world evidence. This debate at the 3rd annual Medical Affairs Innovation Olympics #MAIO2024 in a unique and exciting format with a live poll at the conclusion, features an animated discussion from three speakers: Rashad Massoud, MD, MPH, CEO of Rashad Massoud Associates, LLC., globally recognized healthcare quality expert, physician, formerly visiting faculty at the T.H. Chan School of Public Health; Suzanne Pavon (moderator), Doctor of Pharmacy, Board Member at Iethico, former Vice President of Pharmacovigilance and Quality at Argenx; and Sana Syed, Senior Medical Director - Clinical Lead at Sanofi and public health expert formerly at T.H. Chang School of Public Health. Debate Objectives: ● To discuss the utility of RCTs in research and learning ● To discuss the challenges in translating RCT findings into the real-world environment ● To review the utility of the RCT approach to facilitate real world implementation ● To review the impact of the RCT approach for impact and limitations ● To discuss alternative research methods for research and learning ● To conclude with the research approaches that fit best for clinical trials and the real world; indicating a need for an adaptive, dual approach. 0:00 Alloutcoach Intro Music 0:09 Episode Highlight 3:09 Innovation Olympics Introduction 4:44 Debate Rules & Introduction 6:30 RCTs are the Gold Standard for Research and Learning - For the Motion - Sana Syed 8:12 The Scientific Method - Standard RCT Design 9:46 Rare Disease Case Study 11:38 Translating Biology vs Translating Real World Factors 14:34 Diversity of patients critical for data to represent populations 18:50 RCTs are NOT the Gold Standard for Research: Against the Motion - Rashad Massoud 20:27 Properties of an RCT 21:19 Other Research Questions to Eliminate Other Factors that may influence the results 24:13 Access Questions and Outcomes of Interest - Discovery and Delivery 24:48 Agency for Healthcare Research and Quality (AHRQ) - ~17 yrs to translate data into real world 26:33 Efficacy vs. Effectiveness Research 31:02 Concluding Remarks - case study in which RCT designs are not beneficial 35:30 Question: Health Avatar and AI to create real and virtual control arm Using virtual control arm using real world databases using Bayesian statistical methods 39:23 Case study to emphasize Harnessing Tacit knowledge 42:02 Comment: Weaknesses in generating data we can translate into populations 43:44 Question: Are we creating RCTs from virtual patients or classical RCT design? 47:34 Final Comments - For the Motion, Sana Syed Clinical Studies and Scientific Method - adjustments in diverse patient recruitment tactics 49:31 Final Comments - Against the Motion, Rashad Massoud 53:14 Live Voting Results
Human and veterinary pharmacovigilance (PV) share many goals, challenges and approaches. But there are also significant differences, such as the numerous species and breeds that veterinary PV needs to take into account. In this two-part episode of Drug Safety Matters, James Mount, Veterinary Pharmacovigilance assessor at the Swedish Medical Products Agency, and EU elected chair of the Pharmacovigilance Working Party for veterinary medicinal products, joins the show to talk about veterinary PV practice and its differences and similarities to human PV. Tune in to find out:What are the similarities and differences between veterinary and human pharmacovigilance?How is animal health connected to public health? What types of adverse events are reported on the veterinary side compared with the human side? What can be found in the EU veterinary pharmacovigilance database? Want to know more?The new veterinary medicines regulation (Regulation (EU) 2019/6) can be found here. The public portal of the European Union Veterinary Pharmacovigilance Database.WHO's information page on One Health, an integrated approach to the well-being of people, animals and the environment.A survey of veterinary professionals in Sweden, about current practices and attitudes in relation to adverse events reporting and the accessibility of product safety information.A review of adverse events in animals and children after secondary exposure to transdermal hormone-containing medicinal. A study looking at suspected adverse drug reaction reporting in veterinary free‐text clinical narratives.The EMA Big Data strategy for veterinary medicines in the EU. Data quality framework for medicines regulation | European Medicines Agency (EMA)Small Animal Veterinary Surveillance Network (SAVSNET) - University of LiverpoolVetCompass - Royal Veterinary College, RVCReflection paper on the use of artificial intelligence in the lifecycle of medicines | European Medicines Agency (EMA)The Swedish Medical Products Agency's online reporting form for suspected adverse drug reactions in animals (In Swedish). Join the conversation on social mediaFollow us on X, LinkedIn, or Facebook and share your thoughts about the show with the hashtag #DrugSafetyMatters.Got a story to share?We're always looking for new content and interesting people to interview. If you have a great idea for a show, get in touch!About UMCRead more about Uppsala Monitoring Centre and how we work to advance medicines safety.
In this heartfelt episode of The Patient From Hell, host Samira Daswani talks to Jill Massey, a pharmacist and pharma industry veteran whose path through cancer has been both personal and professional. Jill shares her experiences as a caregiver for her sister, mother, and husband—all cancer patients—before becoming a patient herself. They discuss the emotional and practical challenges of navigating caregiving, the complexities of the healthcare system, and how Jill's unique perspective as both a medical professional and a patient shaped her approach to advocacy, treatment decisions, and resilience. Key Highlights: 1. A Personal and Professional Journey: Jill reflects on how her family's battles with cancer shaped her career in the pharmaceutical industry, blending personal empathy with professional expertise. 2. Balancing Roles and Emotions: From sibling to spouse to patient, Jill shares the unique emotional dynamics of each role and the coping mechanisms she developed. 3. Empowerment Through Knowledge: Jill emphasizes the importance of patient education, advocating for personalized care, and the evolving role of pharmaceutical companies in supporting patient-centric care. About our guest: Jill Massey, PharmD, MBA, BCMAS is Vice President, Global Medical Strategy and Operations (GMSO) for Gilead Medical Affairs. In this role, Jill oversees the Patient-focused Implementation Science team, Medical Strategy and Planning, Insights, Data & Analytics and Digital Innovation, Medical Excellence, Medical Affairs Technology, and Scientific Communications including global publications, medical information, medical external affairs and education, and library and information services. Jill joined Gilead Sciences from Immunomedics where she led the Medical Affairs, Safety and Pharmacovigilance organizations. Prior to that, she led Medical Affairs at Janssen, The Medicines Company and Melinta Therapeutics as well as the Melinta Global Antimicrobial Resistance Program. She began her career in the pharmaceutical industry at Bristol-Myers Squibb Company. Previous to her industry roles, Jill was clinical faculty at the Saint Louis College of Pharmacy, Jewish Hospital and the Program on Aging at Washington University School of Medicine. Jill is a member of the Board of Directors for the Morris County Chamber of Commerce and serves on the Life Sciences Council Steering Committee. She is a member of the National Advisory Committee for the Robert A. Winn Diversity in Clinical Trials Award Program, a member of the Accreditation Council of Medical Affairs Executive Leadership Board and a member of the Seton Hall University Transformative Leadership Advisory Board. Jill earned her Doctor of Pharmacy degree from the University of Nebraska Medical Center and her MBA from Drexel University LeBow College of Business. She completed a residency at Mercer University School of Pharmacy and Emory University. She is Board Certified by ACMA. Jill loves running, baking and spending time with her two kids, Maddie and Alex, and her loved ones, sometimes including her two dogs and cat. Disclaimer: All content and information provided in connection with Manta Cares is solely intended for informational and educational purposes only. This content and information is not intended to be a substitute for medical advice, diagnosis, or treatment. Always seek the advice of your physician or other qualified health provider with any questions you may have regarding a medical condition.
While structured data elements such as patient identifier, medicine name and reaction, are fundamental for adverse event reporting, they may not capture all relevant details. This is where the narrative fields come in, allowing reporters to disclose important contextual information, such as the patient's full clinical course. But how do PV assessors interact with these narratives in spontaneous reports? What needs and challenges do they experience? These and other questions were addressed in an exploratory interview study by UMC researchers Joana Félix and Alem Zekarias. Tune in to find out:What challenges are PV assessors faced with, when working with narratives? How could automation of certain tasks help streamline narrative analyses in the future?How can reporters craft narratives that effectively document adverse events? Want to know more?Pharmacovigilance assessors' experiences interacting with narrative fields in spontaneous reports: an exploratory interview study – poster presented at the 23rd ISoP Annual Meeting “Global Perspectives on Pharmacovigilance in the Digital Age and Advanced Therapeutics”, 1–5 October 2024 Montreal, Canada.Current Challenges in Pharmacovigilance: Pragmatic Approaches, by The Council for International Organisations of Medical Sciences (CIOMS). See page 133 on the role of narratives in good case management practices. Join the conversation on social mediaFollow us on X, LinkedIn, or Facebook and share your thoughts about the show with the hashtag #DrugSafetyMatters.Got a story to share?We're always looking for new content and interesting people to interview. If you have a great idea for a show, get in touch!About UMCRead more about Uppsala Monitoring Centre and how we work to advance medicines safety.
Medication-related-harm (MRH) is especially prevalent in older adults due to changing physiology as the body ages, increased frailty, and the incidence of polypharmacy in this patient group. Giovanni Furlan of Pfizer discusses what makes this patient group so vulnerable to adverse drug reactions, how poor representation and using age alone to define older adults exacerbates this problem, and suggests ways forward in monitoring drug safety in older patients. Tune in to find out:What makes older adults especially at risk of experiencing adverse drug reactions and medication errorsWhy frailty is far more useful than age in predicting adverse drug reaction riskHow pharmacovigilance in older patients may be improved through pharmaceutical practice and better representation in clinical trials.Want to know more?This interview all started with Giovanni's Uppsala Reports article on how age is insufficient a measure of adverse event risk. Read it here.For a summary of the key points discussed in this interview, read Giovanni's paper on the status of drug safety in geriatric patients.If our discussion of frailty piqued your interest, read this paper on the biology of frailty and how this impacts clinical pharmacology, this multi-centre cohort study that shows frailty is significantly correlated with MRH, and this commentary advocating for consideration of MRH as a geriatric syndrome, which needs to be managed as such. As Giovanni mentioned in the interview, Harlan Krumholz was the first to describe post-hospital syndrome. Learn more about this syndrome by reading his paper.For more on prescribing cascades, their prevention, detection, and reversal, read this paper by Brath and colleagues.Join the conversation on social mediaFollow us on X, LinkedIn, or Facebook and share your thoughts about the show with the hashtag #DrugSafetyMatters.Got a story to share?We're always looking for new content and interesting people to interview. If you have a great idea for a show, get in touch!About UMCRead more about Uppsala Monitoring Centre and how we work to advance medicines safety.
Imagine knowing how your body will respond to a medication before you even take it! In this episode, Harsh Thakkar interviews Sarah Rogers, co-founder and president of the American Society of Pharmacovigilance (ASP) and an Assistant Professor at Texas A&M. Together, they dive deep into the fascinating world of pharmacogenomics—how your genetic makeup affects your response to medications. They discuss the challenges of pharmacogenomic testing, real-world examples of adverse drug reactions, and the future of personalized medicine. Chapters:00:00 - Intro00:16 - Breaking Down Pharmacogenomics01:39 - Guest Introduction: Sarah Rogers 02:18 - Explaining Pharmacogenomics to a Child03:24 - Sarah's Start in Pharmacogenomics 04:54 - The Power of Pharmacogenomics07:00 - Pharmacogenomics Clinic at Texas A&M 09:02 - Challenges of Standardizing PGX Testing11:14 - The Ideal Pharmacogenomics Workflow13:39 - Real-World Example of PGX in Action15:44 - Importance of Integrating PGX into EHR 20:24 - AI and Digital Twins in Pharmacogenomics21:52 - Lessons Learned in Pharmacogenomics24:46 - Looking to the Future of Personalized Medicine28:18 - Final Takeaway 30:22 - Outro Connect with Sarah Rogers:- LinkedIn: (https://www.linkedin.com/company/american-society-of-pharmacovigilance) - STRIPE Initiative LinkedIn: (https://www.linkedin.com/company/stripe-pharmacogenomics) - Twitter: (https://twitter.com/amsocietypharm) - Newsletter signup for the American Society of Pharmacovigilance: https://www.stopadr.org/Here are links for some of the information that I mentioned during our meeting:- STRIPE Annual Meeting and Consensus Workshop - (https://www.usp.org/node/289416) - Collaborative Communities: Addressing Health Care Challenges Together(https://www.fda.gov/about-fda/cdrh-strategic-priorities-and-updates/collaborative-communities-addressing-health-care-challenges-together)- Standardizing Laboratory Practices in Pharmacogenomics (STRIPE) Collaborative Community - (https://stopadr.org/stripe)- Texas A&M Interprofessional Pharmacogenomics (IPGx) Clinic - (https://ibt.tamu.edu/cores/Texas%20ClinicoGenomics/Texas%20ClinicoGenomics.html)- National Action Plan for Adverse Drug Event Prevention - (https://health.gov/sites/default/files/2019-09/ADE-Action-Plan-508c.pdf)- Figure showing Stakeholders Involved in the Lifecycle of a Pharmacogenomics Test(https://www.nature.com/articles/s41397-024-00345-y/figures/1)- Subscribe to our podcast for more insights on life sciences:
Disproportionality analyses are a mainstay of pharmacovigilance research, but without clear guidelines, they often lead to confusion and misinterpretation. Enter the READUS-PV statement: the first-ever guide for reporting disproportionality analyses that are replicable, reliable, and reproducible. Tune in to find out: The history of reporting guidelines in pharmacovigilance and why the READUS-PV guidelines were created Why there has been a spike in the publication of disproportionality analyses in recent years and what this means for their reliability What it means to publish “good” pharmacovigilance science Want to know more? Read the READUS-PV guidelines, why they were created, and why they are important. In 2021, Khouri and colleagues showed that current methods and models used for disproportionality analyses are unreliable, and Mouffak and colleagues found that there is a tendency to overstate results in published disproportionality analyses. A book on data mining techniques in Pharmacovigilance by Poluzzi and colleagues delves deeper into this exponential increase in disproportionality analyses. This paper elaborates on the Delphi technique, and how it is used to gather data from reviewers to achieve scientific consensus on a problem. Join the conversation on social mediaFollow us on X, LinkedIn, or Facebook and share your thoughts about the show with the hashtag #DrugSafetyMatters.Got a story to share?We're always looking for new content and interesting people to interview. If you have a great idea for a show, get in touch!About UMCRead more about Uppsala Monitoring Centre and how we work to advance medicines safety.
Send us a textEver wondered how a liberal arts major, MBA holder, and former infantry officer navigates the complex world of pharmacovigilance? Our special guest, Richard Wolff, Vice President of Client Relations at RxLogix, shares his unique journey and insights. Richard's diverse background and rich experience at Johnson & Johnson tackling global adverse event reporting reveal the meticulous precision and regulatory understanding required in the field. Listen as Richard reflects on how fresh perspectives can challenge established processes to enhance efficiency and maintain patient safety.Transitioning into mid-sized companies, Richard discusses the strategic roadmap essential for growth in PV, drawing from his experiences at CSL. He delves into the intricacies of building capabilities, managing inspection observations, and implementing new safety systems while maintaining workforce stability. Hear about his exciting new role at RxLogix and the pressing challenges in the PV industry, including the impact of AI and the complexities of modern PV departments. Discover how regulatory inspections can serve as opportunities for organizational reset amidst increasing complexities.Pharmacovigilance is not without its challenges, and Richard provides an in-depth look at cost pressures, regulatory changes, and the adoption of advanced technologies like AI and RPA. He explores the implications of these innovations on long-term costs and risks, highlighting the necessity of structural innovation to propel the field forward. Uncover the importance of balancing technology with human expertise to ensure patient safety, and how vendors, service providers, and pharma companies must organize effectively to meet rising expectations. Tune in for a riveting conversation on the evolving landscape of pharmacovigilance and the critical role of structural changes in its future.
What is the MOSAIC-NLP project around structured and unstructured EHR data? Why is structured data not really enough for drug safety studies? And to what degree is NLP speeding up access to data and research results? We will learn all that and more in this episode of Research in Action with Dr. Darren Toh, Professor at Harvard Medical School and Principal Investigator at Sentinel Operations Center. www.oracle.com/health www.oracle.com/life www.sentinelinitiative.org -------------------------------------------------------- Episode Transcript: 00;00;00;00 - 00;00;26;14 What is the MOSAIC and LP project around structured and unstructured data? Why is structured data not really enough for drug safety studies? And to what degree is NLP speeding up access to data and research results? We'll find all that out and more on this episode of Research in Action. Hello and welcome to Research in Action, brought to you by Oracle Life Sciences. 00;00;26;14 - 00;00;50;14 I'm Mike Stiles. And today our guest is Dr. Darren Toh, professor at Harvard Medical School and principal investigator at Sentinel Operations Center. He's got a lot of expertise in Pharmacoepidemiology as well as comparative effectiveness research and real-world data. So, Darren, really glad to have you with us today. Thank you. My pleasure to be here. Well, tell us how you wound up where you are today. 00;00;50;14 - 00;01;26;22 What what attracted you in the beginning to public health? Good question. So I trained in pharmacy originally, and I got my Masters degree in Pharmaceutical Outcomes Research at a University of Chicago, Illinois, Chicago. And it's where I first learned about a field called Pharmacoepidemiology, which sort of very interesting to me because I like to solve problems with methods and data and pharmacoepidemiology. 00;01;26;22 - 00;02;00;29 It seems to be able to teach me how to do that. So I got into the program at the Harvard School of Public Health, and when I was finishing up, I was deciding between staying in academia and going somewhere and getting a real job. And that's when I found out about an opportunity within my current organization and I've heard great things about this organization. 00;02;00;29 - 00;02;29;26 So I thought I would give it a try. And the timing turned out to be perfect because when I joined, our group was responding to a request for proposal for what is called a mini sentinel pilot, which ultimately became the sentinel system that we have today. So I've been involved in the Sentinel system since the very beginning or before we began. 00;02;29;28 - 00;03;02;25 And for the past 15 years I've been with the system and the program and because I really like its public health mission and I'm also very drawn to the dedication of FDA, our partners and my colleagues to make this a successful program. Well, so now here you are, a principal investigator. What exactly is the Sentinel Operations Center? What's what's the mission there and what part do you specifically play in it? 00;03;02;27 - 00;03;52;26 Sentinel is a pretty unique system because it is a congressionally mandated system. So the Congress passed what is called the FDA Amendments Act in 2007. And within that FDA, the Congress asked FDA to create a new program to complement FDA existing systems to monitor medical product safety and more specifically, the Congress, US FDA, to create a post-market risk identification and analysis system that will be using data from multiple sources that will cover at least 1 million lives to to look at the safety of medical products after they are approved and marketed. 00;03;52;28 - 00;04;33;07 So in response to this congressional mandate, FDA launched what is called a Sentinel initiative in 2008 and in 2009 as I mentioned, FDA issued its request for proposal to launch the Mini Sentinel Pilot program, and the program grew into the sentinel system that we have today. So it's for my involvement. It sort of grew over time. So when I joined, as I mentioned, we were responding to this request for a proposal and we were very lucky to be awarded the contract. 00;04;33;09 - 00;05;04;05 So when it was starting, I serve as a one of the many epidemiologists on the team and I led several studies and I gradually took on more leadership responsibility and became the principal investigator of the Sentinel Operations Center in 2022. So I've been very fortunate to have a team of very professional and very dedicated colleagues within the operations center. 00;05;04;05 - 00;05;27;26 So on a day to day basis, we work with FDA to make sure that we can help them answer the questions they would like to get addressed. And we also work with our partners to make sure that they have the resources that they need to answer the questions for FDA. And most of the time I'm just the cheerleader in chief just to share my colleagues and our collaborators. 00;05;27;28 - 00;06;11;23 Now that's great. And and then specifically, there's the Mosaic NLP project that you're involved with. What is that trying to achieve and what are the collaborations being leveraged to get that done? So Sentinel Systems has always had access to medical claims data and electronic health record data or year data. One of the main goals for the current sentinel system is to incorporate even more data, both structured and unstructured, into the sentinel system and to combine it with advanced analytic methods so that FDA can answer even more regulatory questions. 00;06;11;25 - 00;06;40;09 So the Mosaic and NLP project was one of the projects that FDA funded to accomplish this goal. So the main goal of this project is to demonstrate how billing claims and data from multiple sources when combined with advanced machine learning and natural language processing methods, could be used to extract useful information from unstructured clinical data to perform a more robust drug safety assessment. 00;06;40;11 - 00;07;21;18 When we tried to launch this project, we decided that we would issue our own request for proposal. So there was an open and competitive process, and Oracle, together with their collaborators, were selected to lead this project. So I want to talk in broad or general terms right now about data sharing, the standards and practices around that. It kind of feels silly for anyone to say it's not needed, that we can get a comprehensive view and analysis of diseases and how they're impacting the population without it. 00;07;21;20 - 00;07;46;15 NIH is on board. It updated the DMS policy to promote data sharing. You know, the FDA obviously is leaning into this. So is data sharing now happening and advancing research as expected, or are there still hang ups? So I think we are making good progress. So I think the good news is data are just being accrued at an unprecedented rate. 00;07;46;17 - 00;08;28;21 So there are just so much data now for us to potentially access and analyze. There's always this concern about proper safeguard of individual privacy. And through our work, we also became very appreciative of other considerations, for example, the fishery responsibilities of the delivery systems and payers to protect patient data and make sure that they are used properly. So you mentioned the recent changes, including in data management, ensuring policy, which I think are moving us in the right direction. 00;08;28;26 - 00;08;56;23 But if you look closer at the NIH policy, it makes special considerations for proprietary data. So I would say that we have made some progress, but access to proprietary data remains very challenging. And the FDA, the NIH policy doesn't actually fully resolve that yet. When you think about the people who do make that argument for limited data sharing, they do mostly talk about what you just said about patient privacy. 00;08;56;23 - 00;09;25;20 IT proprietary data. Pharma is especially sensitive to that, I would imagine. So how do we incentivize the reluctant how can we ease their risks and concerns or can we? Yeah, it's a tough question. I think that this require a multi-pronged approach and I can only comment on some aspects of this. So I would say that at least based on our experience, the willingness or ability to share data often depends on the purpose. 00;09;25;23 - 00;09;55;29 That is, why do we need the data? Many data partners participate in Sentinel because of its public health mission, and our consideration is how would the data be used again, Is there proper safeguard of patient privacy and institutional interest? There are other ways to share data. For example, instead of asking the data to come to us, we can send analysis to where the data is. 00;09;56;06 - 00;10;34;22 And that is actually the principle follow by federated system like Sentinel. So we don't pull the data centrally. We send an analysis to the data partners and only get back what we need it. And it's usually in the summary level format. So that actually encourages more data sharing instead of less sharing. I would say that recent advances in some domains, such as tokenization and encryption, might also reduce some concern about a data sharing, a patient privacy concerns in academic settings. 00;10;34;29 - 00;11;24;26 We've been talking a lot about days, for example, for individual who collect the data and the people I propose to offer them authorship or proper acknowledgment if they are willing to share their data. But that is not sufficient in many cases outside of academic settings. If you look at what is happening in the past ten years or so, there are now a lot of what people call data aggregators that are able to bring together data from multiple delivery systems or health plans, and they seem to be able to develop a pretty effective model to convince the data provider to share that data in some way. 00;11;24;29 - 00;11;55;28 And a way to do that could be to help these data providers to manage their data more efficiently or to help them identify individuals who might be eligible for clinical trials. More quickly. So there are some incentives that we could think of to allow people to to share that data more openly but personally, I think that scientific data should be considered public good and hopefully that will become a reality one day. 00;11;56;00 - 00;12;23;21 Yeah, that's really interesting because it sounds like it's both a combination of centralized and decentralized tactics in terms of of data sharing and gathering. Why is it so important to use unstructured data in pharmacoepidemiology studies? And does NLP really make a huge difference in overcoming the limitations and extracting that data? So in the past, I think that that's true. 00;12;23;21 - 00;12;58;07 Now, many pharmaco epidemiologic studies rely on data. They are not collected for research purposes. So we use a lot of medical claims, data that are maintained by payers. We use each our data that are maintained by delivery systems. So this data are not created for research purposes and much of this data, at least for claim, is data stored in structured format using established coding systems like ICD ten. 00;12;58;10 - 00;13;39;06 Coding system and structured data sometimes are not granular enough for a given drug safety study and certain data or set of variables that are required for claims reimbursements or other business purposes might not be collected at all. And people felt that, well, maybe the information that we need could be extracted from unstructured data because as part of clinical care, the physicians or nurse practitioner or the health care provider might include that information in the notes, but use user data also pretty messy, especially that unstructured data. 00;13;39;08 - 00;14;05;25 So instead of going through the unstructured notes manually to extract this information manually, technique by natural language processing could help us do this task much more efficiently so that we can mind a larger model of unstructured data. Well, obviously, when it comes to real world evidence, you're a fan. Tell us what excites you about using it to complement clinical research. 00;14;05;25 - 00;14;42;07 Get us more evidence based insights and help practitioners make better decisions. Yeah, that's a great question. Yes, I'm a fan of so I personally don't quite like the dichotomy between conventional, randomized, controlled trial and real world data studies because they actually sit along a continuum. But is true that conventional randomized trials cannot address all the questions in clinical practice. 00;14;42;09 - 00;15;30;17 So that's where real data and real data studies come in, because real data like we discussed come from clinical practice. So they capture what happens in day to day clinical practice. So if we are thoughtful enough, we will be able to analyze the data properly and generate useful information to fill some of the knowledge gap. The truth is we have been using real data throughout the lifecycle of medical product development for many years now, ranging from understanding the natural history or burden of diseases to using real data as controls for single arm trials, and that we have been doing this before the term real data became popular. 00;15;30;19 - 00;15;57;11 So I see real data to complement what we could do in conventional randomized trials. So real data studies don't replace clinical trials. I see them to be complementary, and real data studies sometimes are the only way for us to get certain evidence. We already talked about Mosaic and LP that project, but I kind of want to go a little deeper with it. 00;15;57;11 - 00;16;42;02 The idea is to tackle the challenges of using link data structured and unstructured at scale. Tell us about a use case for that project and why it was chosen for this project. We actually, Cerner proposed to use the association between Montelukast, which is an asthma drug and neuropsychiatric events as a motivating example. It is also important to note that the project is not designed to answer this particular safety question, because if you look at the label of Montelukast, there's also already a box warning on neuropsychiatric events. 00;16;42;02 - 00;17;18;26 So FDA already has some knowledge about this being a potential adverse event associated with the medication. The reason why or recalls is has proposed this project was because we actually did look at this association in a previous sentinel study that only used structured data, although the study provided provided some very useful information. We also recognized that certain information that we needed was available in such a data, but may be available in unstructured data. 00;17;18;28 - 00;17;42;18 So if we are able to get more data from unstructured data, we might be able to understand this association better. So that's why this motivating example was chosen. Well, this is an Oracle podcast and Oracle is involved in Mosaic, so I think it's fair to ask you about the technology challenges that are involved in what you're trying to do. 00;17;42;19 - 00;18;17;24 What does the technology have to be able to do for you to experience success? So Mosaic in LP is I was at a very ambitious project because it is using an LP to extract multiple variables that are important for the study. That includes the study outcome, which when you look at it, is a composite of multiple clinical outcomes and it's also trying to extract important covariates that could help us reduce the bias associated with real data study. 00;18;17;26 - 00;19;01;24 So I think technology comes in well is powerful in many ways. First, thanks to technology, the project is able to access very large amount of data from millions of patients who seek care in more than 100 healthcare delivery systems across the country. So this was hard to imagine maybe ten or 15 years ago. But now we have access to lots and lots of data at our fingertips because of advances in technology, because of the large amount and the complexity of the data methods side and LP becomes even more important. 00;19;01;26 - 00;19;33;19 And for this project, we are also particularly interested in whether an LP algorithm developed in one year trial system could be applied to another system, which has been a challenge in our field because each year our system is created very differently. So one, an algorithm that works in one system might not work in another. So we are hoping that through advanced methods and technology, we will be able to address this problem. 00;19;33;21 - 00;19;57;15 So without this technology advances, we might not be able to do this study as efficiently as we could all So the task might might not be possible. So where are we going with this? I mean, let's say the project is a success. What will that mean in terms of the FDA's goals and how NLP gets applied in medical therapeutics safety surveillance? 00;19;57;18 - 00;20;38;03 The hope is that Sentinel system can answer even more questions than it can address today. And the way that we are trying to accomplish that is to see whether or how this complex, unstructured data, we combine it with advanced analytic methods can help us answer questions that could not be addressed by structured data alone. I think through this project we also learned a lot about how the challenges associated with analyzing a very large amount of data from multiple sources. 00;20;38;06 - 00;21;11;14 Again, service data is compiled from more than 100 systems, so it is big but also very complex. And in many of our studies we really need that large amount of data just to be able to answer the question because we may be focusing on rare exposures or real come. So you really need to start with very large from our data just to get to maybe the ten patients that are taking a medication. 00;21;11;17 - 00;21;44;15 And what you learn with Mosaic, can that get applied to addressing other public health issues like disparate ease and asthma diagnosis and treatment, especially when you think about diverse groups? Yeah, that's a great question. So is the project is not designed to address these important questions, but if we are able to better understand the completeness of social drivers of health in these data sources, then we will be able to leverage this data to answer these questions in the future. 00;21;44;18 - 00;22;04;26 I think about how a project like this gets a evaluated at various steps along the way. I guess that's my question. How I mean, what what methods are used to ensure the validity of real world evidence? So the good news is in the past few decades we have been using real data, even though we might not be using the term. 00;22;04;28 - 00;22;36;22 So there's been a lot of progress in the field to improve the validity of Real-World Data studies. So we now have a pretty good framework to identify fit for purpose data, and we also have very good understanding of appropriate design and analytic methods. So to target trial emulation and propensity score methods. So this project and many other projects in Sentinel are following this principle. 00;22;36;24 - 00;23;14;03 And one thing to also note that this project is also following the overall sentinel principle in transparency. So everything we do will be in the public domain to allow people to reproduce, so replicate the analysis. So the protocol is available in public domain, and when we are done with the study, everything will be made publicly available. So that's one way to make sure that the the work at least is reproducible or replicable. 00;23;14;05 - 00;23;43;00 And through that process, we hope to be able to improve the validity of this study. And what about comparisons? How do you compare the results from different data sources like claims data, structured data? You know, I extracted unstructured data, all of that. How was that done, the comparisons? So if you're talking about the Mosaic and LP study, so we have a pretty structured approach to address that question. 00;23;43;02 - 00;24;13;14 So we are using this proven principle of changing one thing and keeping everything else fixed to see what happens. So the project will start by using only claims data to replicate the previously done Sentinel study. And then we are going to add on such data to see whether the results are different. And then we add on an LP extract that unstructured data one at a time to see whether the results change. 00;24;13;21 - 00;24;40;24 So by fixing everything else to be constant and changing one thing, we'll be able to assess the added value of each how data, both structure and structure. And that's how we are going to do it within the Mosaic and LP study. And then what about scalability? How would you make sure the NLP models that you develop are scalable and transportable across all these different health systems of which there are many? 00;24;40;27 - 00;25;10;10 Yeah. The question again is about transport ability. So one thing that is unique about this study, as we briefly discussed earlier, was that the the survey yesterday to actually come from multiple healthcare systems. So the end up models that we are developing will be trained in tune on a sample of patients from this system and not from a single hospital network. 00;25;10;10 - 00;25;42;18 So at the development phase, we are already taking into account the potential diversity of different delivery system. And as part of this project, we also include another delivery system to apply and test the method as part of the transport ability assessment. So we are doing that to make sure that the LPI models that we are developing for this project will be useful for other system as well. 00;25;42;20 - 00;26;12;29 Unknown There is a larger question about computational resources, so that will be the issue that would still need to be addressed because a train and tuning this and NLP models within such a huge amount of data requires a lot of computing resources. So that is something that we could only partially address in our study. But if we want to apply or do the same thing in our system, that would be something to consider. 00;26;13;02 - 00;26;43;13 We talked a little bit about the collaboration with your tech partner, but these things usually have so many stakeholders and disciplines and silos. Tell us first why collaboration is a good thing and unavoidable anyway, and then what the challenges of collaboration are. Maybe some tips on how to best make them work. The problems that we face, at least many of the problems that I face quite complex and they require expertise from multiple domains. 00;26;43;13 - 00;27;18;19 So that calls for collaboration from multiple stakeholders. And we always have our blind spots. So we only see things in a certain way and we always miss things. So that's why I think collaboration is important. But it's really hard sometimes because we all have our priorities and perspectives and sometimes they don't align. And I also learned throughout the years that we don't communicate enough and we may also not have time to communicate or we may be under pressure to deliver. 00;27;18;21 - 00;27;47;21 So all of that sort of contribute to the challenges of collaborating effectively, especially when you collaborate across disciplines, because we might be using different languages to mean the same thing or use the same term to describe different things. So even though we can all speak the same language less English, we might not be talking about the same thing and not communicate at all. 00;27;47;21 - 00;28;17;25 Because because we are using different joggers and terminology. So that has been tough. But I think we are getting better. And so I think that it is for us within the center of operation center, we try to communicate honestly and respectfully and we try to understand different perspectives and we try to find common ground. And but I think ultimately what brings us together is that we have a shared common goal. 00;28;17;27 - 00;28;44;17 A lot of the work that we do. So for music and NLP, we are all trying to answer the same question, which is that how do we use unstructured data and advanced analytic methods to answer safety question? So once we apply on this common goal, things become easier because we start to understand each other better or be able to communicate more effectively. 00;28;44;19 - 00;29;19;16 Just out of curiosity, what are the different stakeholders involved in Mosaic? Who falls on the roster? we have people from different disciplines, so we have experts in natural language processing and artificial intelligence. We have epidemiologists, both statisticians, clinicians, we experts in psychiatric conditions and respiratory disease. We have data scientists, we have engineers, we have project managers. So it's a very big group of individuals with different expertise in this project. 00;29;19;18 - 00;29;46;14 Well, you probably noticed Oracle's really thrown itself into and committed huge resources to health and life sciences. Things got really exciting with the acquisition of Cerner and Cerner and Visa. What's Oracle doing right and what do you think it should be doing to make itself even more valuable in health and life sciences? Well, this is a great but very difficult question, so I cannot comment too much what Oracle is doing or will be doing. 00;29;46;17 - 00;30;23;06 But I can say more generally that there have been a number of technology companies that have tried to foray into health or life sciences. I would say with mixed results. And one reason is that our health care system remains highly fragmented and complex, so it takes a lot of energy to break the status quo. So you probably know that we were one of the last countries in the world to transition from ICD nine to ICD ten coding system, and we are soon going to move into the ICD 11 system. 00;30;23;06 - 00;31;00;05 So I'll be interested to see whether the US is ready for that. And that again, is maybe a reflection of just how complex and fragmented our system is and disruptive innovation and I think are great, but they may or may not translate into successes when they applied to health care. That is not to say tempesta mistake. I'm actually pretty optimistic that the perspectives and solutions and ideas brought by technology companies could help us solve a lot of problems that we have today. 00;31;00;07 - 00;31;31;26 But I think that it will be good to engage people who will be struggling with these issues early on and to work together with them to develop solutions that are not just good on paper, but also feasible in practice. So at least in my very limited experience, we have seen some very cool technology that ended up not being useful for health care just because it's very hard to change what people have been doing. 00;31;31;28 - 00;31;56;09 So again, disruptive innovations are good, but sometimes it's just very hard to adopt, at least not quickly enough for for us to see meaningful changes. Yeah, that's really fascinating. It's, you know, it is disruptive innovation, but it's not always applicable to the to the goals you're pursuing. But it does feel like technology where that's concerned, the future is coming at us faster and faster. 00;31;56;11 - 00;32;32;21 So what are the technologies that are most interesting to you? Is it A.I. or what big advances in public health do you see coming? Maybe sooner than we thought. Yeah. Yeah. You know, I feel like you said some of this came too fast. Like, I wish I. And closer to retirement, I don't worry about this. But so even though I say disruptive innovation sometime might not work in health care, but I will say generative A.I. seems to be a recent exception. 00;32;32;24 - 00;33;10;14 So I would say that generative is definitely on the list of things that surprised me in a very nice way. I will also say that the continue fast accrual of better real data is also something that excites me and the continue recognition or increased recognition of the potential real data of. It's also something that I think is good to have for things that came sooner than I found it again, generative. 00;33;10;19 - 00;33;44;13 AI So if you ask me when, we'll be ready for generally. AI Last year or two years ago, I would say not yet, but now we in the era where everything seems possible. So I remain extremely optimistic about generative in some of these last language models that will help us analyze unstructured data even more efficiently. Well, therein it's deeply fascinating and exciting stuff. 00;33;44;14 - 00;34;10;27 Thanks again for letting me pester you with these questions. If our listeners want to learn more about Sentinel, Operation Center or Mosaic or you, what's the best way for them to do that? So Sentinel has a poverty website where we post everything that we do. So is Sentinel initiative dot org. So I am a member of the Department of Population Medicine at Harvard Medical School. 00;34;10;29 - 00;35;00;16 So our website's population is a thought, but these would be two places that would be very informative for audience. Who wants to know more? All right. We appreciate that. And to our listeners, go ahead and subscribe to the show. Feel free to listen to past episodes because they are free. There's a lot to learn here. And if you want to learn more about how Oracle can accelerate your own life sciences research, just go to Oracle dot com slash life dash sciences and we'll see you next time on Research in Action.
How do clinical research funders operate? Why do patient-centered outcomes matter so much and improve the quality of research? And how is patient-led research being applied to clinical care? We will learn all that and more in this episode of Research in Action with Greg Martin, Chief Officer for Engagement, Dissemination, and Implementation at the Patient-Centered Outcomes Research Institute (PCORI). www.oracle.com/health www.oracle.com/life www.pcori.org/
Ensuring trust in AI is vital to fully reap the benefits of the technology in pharmacovigilance. Yet, how do we do so while grappling with its ever-growing complexity?This episode is part of the Uppsala Reports Long Reads series – the most topical stories from UMC's pharmacovigilance news site, brought to you in audio format. Find the original article here.After the read, we speak to one of the authors of the article, Michael Glaser, to learn more about how AI and ML has been used in pharmacovigilance so far, and what needs to happen to ensure its continued use in the field.Tune in to find out:● How AI and ML are being used today in pharmacovigilance processes● Why a mindset change is necessary to make full use of AI/ML in pharmacovigilance● How we may best move forward to implement AI/ML into healthcare. Want to know more? To know more about how AI and ML are being used in pharmacovigilance currently, read this scoping review.To know more about future trends of the use of AI in Biopharma, read this Accenture survey.Despite there being major interest in ML and AI to do more than task automation, there are a number of barriers to its implementation in healthcare. Check out this future-focused paper on the use of AI/ML in pharmacovigilance that details how to utilise it to its fullest potential.A mindset shift is necessary in terms of how we think about data, in terms of sharing, how to generate data required to effectively train AI/ML models.A validation framework must be developed for AI-based pharmacovigilance systems. One suggestion is to do so using a risk-based approach.While there is much interest in using recently developed AI technologies such as chatGPT, preliminary studies like this one suggest that the technology has a ways to go to be useful in pharmacovigilance.The World Health Organization have published an extensive guideline on the ethics and governance of AI for health.Join the conversation on social mediaFollow us on X, LinkedIn, or Facebook and share your thoughts about the show with the hashtag #DrugSafetyMatters.Got a story to share?We're always looking for new content and interesting people to interview. If you have a great idea for a show, get in touch!About UMCRead more about Uppsala Monitoring Centre and how we work to advance medicines safety.
Look-alike medicines, unclear communication and distractions during administration – medication errors may occur for many different reasons. They all have in common that they are unintended mistakes in the drug treatment process that may or may not lead to patient harm. In this episode Ghita Benabdallah and Loubna Alj from the national pharmacovigilance centre of Morocco, and Alem Zekarias from Uppsala Monitoring Centre discuss how we can prevent medication errors from occurring – and, when they do occur, make sure that they are reported as such. Tune in to find out:What are the most common causes for medication errors?How should strategies for preventing medication errors be devised? How does the assessment of suspected medication error reports differ from “regular” ADR signal assessment?What can be done to encourage healthcare professionals to report medication errors?Want to know more?In March 2024, WHO published this systematic review of the global burden of preventable medication-related harm in healthcare.According to this 2021 article in BMJ, an estimated 237 million medication errors occur in England every year. Avoidable adverse drug events were calculated to cost the National Health Service an annual sum of GBP 98 462 582 per year, consuming 181 626 bed-days, and causing/contributing to 1708 deaths. This 2012 meta-analysis confirmed what had been suggested in several observational studies: that preventable adverse drug reactions are a significant healthcare burden.The European Medicines Agency (EMA) has a dedicated webpage with recommendations, guidelines, legal requirements and a good practice guide on medication errors. Join the conversation on social mediaFollow us on X, LinkedIn, or Facebook and share your thoughts about the show with the hashtag #DrugSafetyMatters.Got a story to share?We're always looking for new content and interesting people to interview. If you have a great idea for a show, get in touch!About UMCRead more about Uppsala Monitoring Centre and how we work to advance medicines safety.
How can shifting mindsets fuel the next wave of innovation in the pharmaceutical and life sciences industry? In what ways can we ensure the vast amounts of health data are utilized securely and effectively to foster groundbreaking medical advancements? And how is Oracle's new Health Data Intelligence poised to transform the industry in an unprecedented manner? You'll learn all that and more with our guest Michael Fronstin, Vice President and Chief Commercial Officer at Oracle Life Sciences, who has worked across nearly every area of the industry from positions at Merck to J&J to Kantar Health and now at Oracle. -------------------------------------------------------- Episode Transcript: 00;00;00;04 - 00;00;26;25 In what ways do the mindsets in the pharma industry need to change? How can we make sure massive amounts of health data is applied to practical effect? And how might Oracle's new Health Data Intelligence platform be an unprecedented game changer? We'll find all that out and more on Research in Action. Hello, welcome to Research in Action, brought to you by Oracle Life Sciences. 00;00;26;25 - 00;00;49;15 I'm Mike Stiles. And today we've got a guest who's been a veteran in the life sciences industry and who knows Oracle Life Sciences quite intimately because the guest is Michael Fronstin, vice president and chief commercial officer at Oracle Life Sciences. He's worked across nearly every area of life sciences, from positions at Merck to J&J to Kantar Health and now at Oracle. 00;00;49;15 - 00;01;11;25 So, Michael, thanks for being here. Thanks, Mike. Happy to be here and thank you so much for hosting this session. Really appreciate it. Great. Well, you know, you're the perfect person to talk to about what I want to talk about, which is changing people's minds and changing how we even approach and think about life sciences. So you've got that to look forward to. 00;01;11;25 - 00;01;34;28 But first, let's learn a little bit more about you. How did your interests and opportunities in life take you down the path that led you to where you are now? Yeah, thanks for that question. That's that's a great question to start out with. I'll tell you that as human beings, we all have something going on in terms of health care, whether it's impacting ourselves or friends or family, everyone's going through something. 00;01;34;28 - 00;01;56;25 At some point. You just don't know what the magnitude is or how long lasting, right? So having patience and empathy is so important. And of course myself, I've gone through things and unfortunately starting at a very early age of 12, I lost my best friend to the brain cancer and from the time I was 12 to the time I was 21, unfortunately, I lost a lot of people to different health ailments. 00;01;57;11 - 00;02;17;10 I guess, climaxing with losing my father when I was 21 years old. During that time, I always thought about health care and how it was impacting the people around me and wondering what could I do? And I felt pretty helpless, to be honest with you during those times, because some young boy don't there and there really wasn't anything I can do. 00;02;17;10 - 00;02;35;01 But as I got older and I went into college, I realized I could make a difference in health care. And that was going to be the industry that I was going to focus on. So I went into social sciences, became a sociologist with a business math background, and went to graduate school for an MBA in health care arbitration. 00;02;35;10 - 00;02;56;07 And that's when really things opened up to me where I started saying, okay, what aspect do I like? Where can I make a scalable impact? And I ended up joining Humana A down in Florida for a year or so, realizing that I can make a difference there and get people enrolled, help them get claims processed and paid. And from there my career took off. 00;02;56;07 - 00;03;21;02 I end up going to Merck, carried the bag and really experience the in office experience back in the days of the early nineties in terms of what patients were experiencing, seeing doctors who were really, really good and so much good at diagnosing patients and treating them in a time where most of the chronic conditions didn't have treatments available and new ones were coming out. 00;03;21;16 - 00;03;53;06 And I'll tell you, it was pretty exciting during these times being at Merck and seeing all these innovations. But I'll tell you, during that time I was really able to focus on one therapeutic area and it wasn't very scalable. It wasn't really having the impact it wanted. And it wasn't until I came to the consulting side of the business, you know, working with dozens of customers and maybe hundreds of brands over the past 20 plus years where I really felt like maybe a direct and indirect impact on people's lives around the globe. 00;03;53;28 - 00;04;16;02 So that's that brings me to today. And now I'm with Oracle Life Sciences, where I feel like it's even bigger and broader and better. So I'm excited about the present. I'm excited about the future. Yeah. You mentioned you kept repeating a phrase that kind of struck stuck with me, which is that you wanted to make a difference. Is that hard to do in the health care space? 00;04;16;02 - 00;04;39;12 I mean, have you been gratified by your ability to do that or has it always been a push and pull? Oh, interesting question. Definitely a push. And so, you know, sometimes you can you can make decisions and get them executed very quickly. Other times, it takes a while to do. You know, you have regulatory bodies that you have to deal with different types of payers around the world. 00;04;39;22 - 00;05;04;19 Decisions are always made quickly. And if it's the right decision because of various reasons, whether it's bureaucracy or internal or external, or you need to generate real world evidence modeling or even publications, we have more than 2000, maybe 3000 publications, and you develop the evidence, you submit the publication. It could take, you know, six months, a year, two years to get it published right? 00;05;04;19 - 00;05;24;14 So things just take time, unfortunately. But yeah, you can make a difference. I feel like I've made a difference. I feel pretty gratified about what I've done. And in the areas of the impact that I've made. So and a lot of it is just make an impact within your world and hoping that you can expand it beyond to make a broader impact. 00;05;24;14 - 00;05;59;11 You were at Kantar Health for like 17 years or so. How did what Kantar does align with Oracle Life Sciences and the idea behind just leveraging technology to benefit customers and partners? I'm actually coming on 19 years since we think about it and you mention it. So when I step back and think about my time at Bert or Change in Merck and the broader industry, life science clients need to accomplish three things in order to get their compound, whether new or existing compound, really the new compounds into the hands of the appropriate patients. 00;05;59;11 - 00;06;24;18 They need to get their drugs approved right by some regulatory authority. They need to get them reimbursed and they need to have a strong launch to drive awareness. Otherwise no one's going to prescribe it or patients. People aren't going to request it, right. So those three things need to need to occur. Kanter Health is really focused on the second and third in terms of the research services and expertise. 00;06;25;00 - 00;07;10;02 So the types of people are. Kanter Help are methodologies, social scientists like epidemiologists, psycho nutrition, these these are the folks that know how to design and conduct research, how to consult on the research from a Real-World evidence perspective and driving insights, evidence from a commercial planning perspective, prioritization, things like that. Where is the Oracle Life Sciences group? The other side of the group is really all about technology and applications predominantly focused on driving clinical trials for regulatory approval, of course, and in the area of pharmacovigilance during those trials and tracking them when those products are in the real world. 00;07;10;06 - 00;07;38;08 Right. Post-marketing authorization. So when you bring these two groups together and these types of people together, the technology, the medical intelligence, the scientific, methodological experience of the cancer health folks, have you got the best of all worlds, right? Technology, data experience combined. You take these wraparound services with the technology in and now our clients are able to see a much higher level of value, if you will. 00;07;38;23 - 00;08;02;25 Well, you've actually been anything but shy in the past about saying how the mindsets in the pharma industry really need to change. So what is the current mindset? And in what ways is it limiting? I'll tell you, the health care industry, including life sciences, has always been a little bit of a laggard in terms of of our movement. 00;08;03;11 - 00;08;30;15 Part of that issue is that we we operate in silos, right? And even within our life science clients or customers, the different cross-functional teams don't always come together. They don't know each other. Sometimes they buy the same data, right? So the inefficiencies of spending more budget than they need to, we're not leveraging the same data for different purposes, and we really need to break down the silos. 00;08;30;29 - 00;08;53;15 I think that from a mindset perspective, individuals on every side of the business really need to step back and pick up their heads and look around, see the big picture, understand where are we going? The data is critically important. Big data was becoming the buzzword ten, 15 years ago, but no one really knew what that B meant. Well, now it's here. 00;08;53;22 - 00;09;14;06 We could do something with big data, right? Is sort of on the fringe. Some people are using it, some people aren't, there hasn't. So this is a time where you could either bury your head in the sand because you don't understand it or you're afraid of it, or you can lean in and figure it out. And if you don't lean in, you're going to be left behind. 00;09;14;06 - 00;09;45;01 So I think we need to break down the silos. People need to step back and see the big picture. And I think they need to take risks and and lean in and it Oracle, that's what we're doing. We're committed to helping, you know, through creating open ecosystems, to breaking down barriers across teams, using our teams. And, you know, hopefully everybody will wind up picking your head up and looking at the big picture and caring more about collaboration and how these things can improve so that innovation moves forward faster. 00;09;45;17 - 00;10;06;25 Is that a realistic ask? I mean, I assume researchers are very busy with their heads down working on what they're working on. Can they can they expand and broaden their view? They have that luxury, Absolutely. It's like anything else, you just have to make the time. You got to take the time to make the time, invest the time to figure it out. 00;10;06;25 - 00;10;26;26 It's not easy. And I'm not saying it's easy by any means, but it's worth it to do. And I remember when I was a rep with Merck, you know, moving to Pennsylvania, the Home Office, the analysis, one of my problems that you get there and if you want pieces of advice when you get there, keep your head up. 00;10;27;13 - 00;10;51;11 And I said, I'm always positive. He said, that's not what he said. Look around, understand what's around you, incorporate it, immerse yourself in things you don't understand. You know, be comfortable being uncomfortable and again, new job, new new house placeholders. How do we find the time, how to figure it out? Right. And I see the people around me and our clients. 00;10;51;11 - 00;11;18;18 I see the people around me at Oracle Life Sciences. The ones who are doing that are the ones that are being most successful. Yeah, I love that. Get, get comfortable being uncomfortable. That's not something people dive into, as is uncomfortableness. But, you know, I don't care if it's industry, politics or even favorite flavor of ice cream. Getting anyone these days to change their mind or change their mindset is really hard. 00;11;18;18 - 00;11;49;09 So getting an industry to collectively think differently, that can't be easy. So what do you see as the biggest challenges to that? And is it that there needs to be some driving force for that? And is that the role Oracle's trying to play? Yeah, it's not easy for sure. All right. So some of the biggest challenges are really the cultures that are existing within and across the industry where people are so busy, right? 00;11;49;11 - 00;12;16;11 They're not set up to work. Cross-functionally The siloed nature that's that's occurring across our industry, even in between clinical care and clinical research, there are gaps. So I think all these different places are causing, you know, challenges in terms of making a difference, getting immersed and taking those risks. People aren't always rewarded for taking risks. So let's say it happens. 00;12;16;11 - 00;12;40;29 Let's say there's a shift in mindset and we're thinking more about leading with knowledge and information and looking at that big picture. What opportunities does that present for both the industry and for me when I get sick? Yeah, no, that's a great question as well. So for the industry, I think we'll be able to actually bring compounds to the to the marketplace more quickly. 00;12;41;10 - 00;13;30;00 Right. For you as an individual or us as individuals, all of us will be able to have more options, both clinical research as a care option. Right? Right now, only 3% of eligible patients participate in a clinical trial. Right. If we're able to take information and put it back in the electronic health record or h.r. System so that doctors can look at it at the point of care and make decisions whether it's about what is your care that they want to prescribe or it's about how are these products impacting you as a patient from a pharmacovigilance or really a tolerability or safety perspective, they're able to adjust very quickly right there on the fly, right? 00;13;30;00 - 00;13;51;29 They'll have more data at their fingertips, as we put it in. And that also could be recruiting patients into clinical trials. Right. So they don't know what's the inclusion exclusion criteria. Look it up. So how can you at their fingertips and knowing that this patient can just walk in the door for these patients scheduled to walk in this week, they're eligible. 00;13;52;00 - 00;14;12;02 Let me make sure that I talk to them about that so that they have other options that will help them get well. Yeah, So it's a good payoff. Your answer to this can be Mike, why don't you just mind your own business, but ask Oracle recently combined their Oracle Health and their Oracle Life Sciences divisions. Why did they do that? 00;14;12;11 - 00;14;37;06 Well, I'll tell you, I won't tell you to mind your own business. This is sort of the the biggest payoff I think we're seeing is movement that we've seen in health care. So the acquisition of Cerner by Oracle was just enormous. And it Cerner, these are your cancer health group is part of it really also was a big deal, right? 00;14;37;12 - 00;15;06;10 Because now we can take what's happening in health, in the clinic, in the hospital, in the offices and combine it with life sciences. Everybody has the same goal, which is to save lives or to increase quality of life of patients. But both of these groups, the hospital systems around the world and the life science companies around the world, they're not connected, right? 00;15;06;10 - 00;15;40;22 They want to be connected. They want to intersect, but they're working in silos, trying to influence each other when they both have the same goals, which is to save lives or help people. And now with Oracle Health and Oracle Life Sciences being under not only the same umbrella of Oracle, but under the same leadership in terms of team of firms, we're able to break down the silos so that we're able to share the appropriate data and information in an open equal ecosystem in bi directional way. 00;15;41;11 - 00;16;09;04 And while these two groups are deeply intertwined, yet this distinct, if you will, there are innovations there that we're looking at that will help everybody that some of the migrations celebrate recruitment, sharing of data, point of care decisions, things of that nature. So it's about turning data into information, that information into insights with some kind of open, intelligent, cloud based platform. 00;16;09;27 - 00;16;39;24 There is the problem, though, of drowning in data, but starving for insight that's applicable to so many businesses across so many industries. How would the ecosystem that you just described keep life sciences customers from drowning in data that is never used for practical effect? They're absolutely drowning in data. There are more data sources existing secondary data sources in the industry and across the world today. 00;16;40;05 - 00;17;12;02 The majority of these like probably 98% of them are not unified, they're not connected, and interoperability is lacking. Credit card companies figured it out a long time ago when healthcare has and we're starting to get there. Training unified platform of data Health data intelligence platform is what we call it in Oracle, backed by the Oracle cloud infrastructure. So you have data that's very sensitive sovereignty of nations, you're using it. 00;17;13;04 - 00;17;58;11 And of course OCI, Oracle Cloud Infrastructure affords the opportunity for security and speed and all these other benefits. So the more of tokenization we could do to connect the charged with other h.r. Claims with patient reported outcomes survey. The more we can do that in standardized ways with the right governance will help our clients sort through this sea of information so that we can and will help them, of course, you know, focus on what's important, you know, and use A.I. to define the trends in predictive analysis, what predicts better or worse outcomes. 00;17;59;01 - 00;18;21;22 So it's going to take time. We're getting there. We're already making a lot of progress, but I think that's now how we're going to help our clients get there. Well, I did ask about the obstacles of changing overall mindsets, but what are the remaining obstacles to actually building and implementing this eco system that you're talking about? Are there remaining tech obstacles? 00;18;21;22 - 00;18;50;01 Are there privacy issues? I mean, what's what's there that's making this a tough job? Not only we drowning in data, we're drowning in obstacles like that. So certainly you know, that's an obstacle of legalities around the world. Cultural changes and mindsets. Like we mentioned, there's governance. Who owns the data? We get data right to the data technology. Then we go back to that for a second. 00;18;50;11 - 00;19;13;28 You know, how do we connect from one system to the other? I do believe there's still 300 EHR systems out there. The interoperability, governance image. I mean, we're just not sure about. Also, we got to kick them off one at a time. And you know what we're doing at Oracle and Oracle Life Sciences is we're partnering with a lot of different organizing that's out there. 00;19;14;06 - 00;19;50;17 You might have seen our partnerships with the video code here. Johnson Labs, from algorithms, Perspectives. We're partnering with a lot of other organizations to help chip away at these obstacles and get to this ecosystem that we're talking about, where everybody wants. Yeah, you know, when you when you list those obstacles, one thing that's not there is resistance by patients, because I think most of us, you know, it's kind of a joke amongst everybody how every time you go to the doctor, you fill out the same forms again and again and again and again. 00;19;50;27 - 00;20;14;15 Clearly, there's not any kind of centralized clearinghouse for data on me as a patient. And I think most of the public kind of What's that? What's your view on meeting patient expectations where that's concerned? You know, isn't that the most important thing right of the whole conversation is putting the patient at the center of meeting their expectations. Okay. 00;20;14;15 - 00;20;41;03 There are a couple of countries where this is already occurring with the patients. The is all in one place. The patient just pulls up their app and they go and it doesn't matter which doctor or hospital you're walking into or what country they're visiting when they're traveling, they have their medical records in their pocket. One of the articles of issues is around privacy, and you might have mentioned this. 00;20;41;16 - 00;21;12;16 So in the US we have hip in Europe yard and this is trying to protect the patient for the right reasons. But we have to and we have to work within these systems to make sure we're able to operate together for the patients. There's nothing more annoying walking at your doctor's office and filling out the same or complaint or consent form or insurance form or whatever it is, you know, and it's certainly something that we need to do. 00;21;13;04 - 00;21;46;07 I think from a cohort perspective, the older populations and I'm not sure where that likes it's at 40 or 50 or 60, I think they're a little bit more protective and reticent about their privacy and their information. Whereas I see the younger generations, they're like, it makes sense to share it all the time. I wanted out less concerned about privacy, and maybe it's because of how they've grown up with the apps, social media, you know, everything's out there, you know? 00;21;46;08 - 00;22;09;17 So I think the trend is here and the tide is turning. You know, we have to find ways to continue to meet the patients and people where they are. Well, I'm sticking with that patient theme. There's how patients are involved or not in research. And we are hearing more about patient centered outcomes in research. It's another kind of mind shift that needs to happen. 00;22;09;17 - 00;22;35;04 How are we moving toward that where we're listening to the patient more and involving them more in clinical research than we used to? And that's that's the next great question. You threw that statistic out there that what, there's like 30% participation? I mean, there's clearly an issue. Yeah, Yeah, for sure. So patient reporting outcomes are typically subjective nature, right? 00;22;35;04 - 00;23;06;29 So by developing different instruments and scales that derive or predict something in might predict undiagnosed insomnia or anxiety, depression might predict of control of asthma, things of that nature. But there's typically surveys that have been validated through different types of behavioral science, a cognitive interviewing techniques, things of that nature, and then putting them out there. Right. And there's pros and there's observables, which are caregivers, right? 00;23;06;29 - 00;23;33;29 So someone caring for an adult relative, they're scales like that around caregiver burden, these sorts of things. And I'll tell you that the FDA has made a concerted effort to focus on patient focused drug development, and they've put these guidelines out there in terms of what they expect as websites companies are going through their clinical trial or clinical development programs. 00;23;34;00 - 00;24;01;17 Right. So I think that was a really great step to say not only open to this, we want it, we expect it. Right. So we've seen some of that, too. Now get your question. How do you do it right. Well, you can go with it. You charge it claims and look at information about the patient. But you also need to go directly to the patient and get their voice so you can do qualitative types of exercises. 00;24;02;04 - 00;24;22;21 For us at Oracle, I think this live of voices two trials where we go out to cohorts of patients who are eligible and we run through issue friendly terms the inclusion exclusion criteria. What do you think? Would you participate or not? What do we need to change here? And there's a whole bunch of other things to expose them to. 00;24;23;04 - 00;24;46;07 And then they tell us just no way, and this is impacting them. Phone calls of various clinical trials that our clients are working on, and they're taking it back to the EMA, the FDA, and say, here's the patient's voice and this is why we're making the decisions so that we're representing what these patients want in our trials. And often it's different. 00;24;46;23 - 00;25;13;08 So that that's one way We're also seeing more decentralized clinical trials. So over the past four years, with all the challenges of leaving one out and going to a site DCT decentralize, some trials have really accelerated in terms of the volume of trials. So so no longer just a patient have to drive an hour or 4 hours or however far to a site. 00;25;13;21 - 00;25;41;10 Now you bring the trial to them. You bring the phlebotomists to their house, you send them the wearable technologies or whatever it is they might need. So you're meeting the patients where they are so that you could increase participation and be more efficient, more productive, and really get it done in a better way. And the last thing I might mention is some natural history of disease registries. 00;25;41;21 - 00;26;07;25 These are registries that occur usually before the product goes into phase two or phase three clinical trial. And this is where you really start to understand what is the natural history of the disease. Most important, rare diseases where it could take years and years to get a count out in development compounded through or me to diagnose the patients. 00;26;08;04 - 00;26;33;01 And it takes too long to do that. So understanding the natural history of disease is critical. Right now we're running a global registry called Guardian, which is in Gauci Disease type two and Type three, and this registry is the Guardian Registry Registries one. We're collecting patient and caregiver information. We're actually developing a new approach and a new ops or so. 00;26;33;01 - 00;27;04;27 We'll have the patients voice. There are no products indicated for type two or Type three. So all the information is being fed back to the clients who have compounds in development for consideration in their clinical trials. And we're working with the International Gaucher Alliance, which is the global patient advocacy group on this registry. So it's a great partnership and it's getting that patient's voice, you know, where it needs to be, which is in the hands of of the compound development. 00;27;04;27 - 00;27;29;14 You mentioned A.I., you touched on that a little bit at AEI has certainly become part of the conversation, thinking about how it is or has the potential to impact therapeutic research and development. What, in your view, is and isn't overhyped about A.I. and the different stages of research and getting drugs to market so much? I make a lot of a lot of hype. 00;27;29;20 - 00;28;10;01 But also there's there's a lot of there's a lot of sizzle and there's a lot of sauce, right this. So you have to look for it and find it. So reading articles about organizations like Genentech and Janssen who are doing what's called Lab in the Loop, right. And a lot of a lot of life science, pharma companies and biotechs are doing this now where they're doing a and they're crossing their existing and other contacts with biological databases to uncover where might there be a match where some combination of a compound or multiple compounds could actually influence some disease? 00;28;10;01 - 00;28;47;05 Right. And then they tested they put it back in. So that's one area where we're seeing a lot of activity with with a for sure, critical trial designs, just looking at feasibility and protocol optimization and to understand where are the patients, how we are and how they're helping with patient recruitment. Where is indentify sites identifying the patients and incorporating dashboards back at the sites to help doctors identify and quickly recruit those eligible patients, or at least to have the conversations to see if they're interested. 00;28;47;14 - 00;29;28;22 Understand diversity of disease using various databases that have social determinants of health to make sure that we're diverse. Once the FDA is draft guidances, which which looked at everything from social determinants and ethnicity to co-morbidities, other demographics, transplantation, patients, etc., etc., etc. real world evidence teams are using it for their literature reviews. Unfortunately, sometimes they come across hallucinations or some false references, you know, show up and therefore you're always going to need this human collaboration to make sure your data is reliable. 00;29;29;03 - 00;29;55;07 And I'd say the last thing my head is pharmacovigilance, where we can go into existing databases, e charts, claims, both structured or unstructured notes, I should say, you know, and pull out information to identify patients who are having issues and report it in some sort of rapid or real time reporting and not wait. So out a major issue? 00;29;55;19 - 00;30;18;15 Well, since the listeners have been interested enough to still be listening, let's reward them by diving deeper into some of those specific technologies for clinical trials. What is Oracle's role in helping with randomization and trial supply management, which I think is also known as interact of response technology? Again, the work being done to that to get to therapeutic breakthroughs faster. 00;30;18;27 - 00;31;04;00 Yeah. Or TSM randomization, trial or supply management and ERP. It used to be called priority and now it's our TSM. This is an area where we've been playing for a long time. Continue to look at our tools for our clients so that they're able to do things that are quicker, faster, more efficiently. And certainly we've invested in a number of new people around the organization in our data product team, which is made up of some phenomenal engineers, you know, and they're investing we're investing significantly in our technologies to bring it to the next level and clients are responding appropriately, which is which is great. 00;31;04;03 - 00;31;30;08 And it's in a scenario where it's going to help clinical trials more quickly and more efficiently. So amazing things are happening. But, you know, I'm never satisfied. So I'm always curious about what the future could hold. I mean, we already touched on A.I., but what trends and technologies are you seeing out on the horizon that are most likely to bring us the kind of health care revolution that we think is possible? 00;31;31;11 - 00;31;55;06 Well, we've talked about some of them, this change in thinking culture for sure. Some of the policy and privacy types of things that we need to to get through. But this is what's not only on the horizon, but is here, right? It's here right now. I'm excited about the things that we're doing with Oracle Life Sciences to get there faster. 00;31;55;18 - 00;32;30;27 You're combining the data, our medical intelligence for our clients, just seeing it all in one place so that our customers are able to leverage it in a way, giving back to a future for physicians to close that gap between clinical research and clinical care. I think that's what I'm most excited about, I suppose. Oracle recently, very recently announced Oracle Health Data Intelligence, which is being called an open intelligence ecosystem or innovation. 00;32;31;09 - 00;32;57;21 Talk about what is that and how that helps life sciences. And researchers love to do so. So first of all, the Oracle Health Data Intelligence platform, it's open. It's open to anyone, meaning that anybody could tap into it, regardless of what industry, what part of the health care industry or working life sciences, whichever system, electronic health record system you're you're using. 00;32;58;06 - 00;33;32;24 So it's really flexible from that perspective that anybody can tap into it. And the data is research ready, meaning it's usable, right? We're form forming it, we're standardizing, and we're harmonizing it in a way that you can go and do the research that you need to do and get the insights and generate the evidence that you need. And this will help in such a tremendous way with the challenges that I mentioned earlier, breaking down silos, connecting disparate data sources, being structured and data that's now usable. 00;33;32;24 - 00;33;59;14 Right? That is that is not usable currently and it's in many formats. So customers will be able to or anyone really can tap into usable data sets from thousands of sources. So that's the other thing anyone can participate, contribute data. We're going to pull in data from a number of different places and again, turn that data into information and that information into insights and that insight those insights into evidence. 00;33;59;26 - 00;34;23;25 So and this will include longitudinal health data, real world data. I didn't define real world data, so real world data is basically any data that is not clinical trial data. It's in the real world, right? So you see that the care that's occurring within the physician's office or hospital that's not part of a clinical trial is considered real world data. 00;34;23;25 - 00;34;48;01 So that's longitudinal health data, electronic health records, patient registries, whether it's natural history or safety, product registries, all that is considered real world data. And all of that will be part of the health data intelligence platform. And this is an API driven ecosystem, which means anyone could access it. As I mentioned before, whether you use an Oracle clinical application or not. 00;34;48;27 - 00;35;16;18 And you can rest assured knowing it's running securely and safely on the Oracle Cloud infrastructure and as you know, OCI Oracle cloud infrastructure, not only is it safe and secure, but it's a military grade infrastructure and it's being used by the Department of Defense. So you could trust it is reliable, scalable, and it's getting the job done. And the health data intelligence platform, as you know, we have it, we're building it, improving. 00;35;16;18 - 00;35;36;17 This is really a big part of our future here in Oracle Life Sciences at Oracle and quite frankly, in the broader industry. Well, great. You know, I got my answers. Thanks for being our guest today, Michael. We'll be watching those, watching for those shifting mindsets and the changes coming to life sciences. Certainly, Oracle seems to be leading the way in that area. 00;35;36;28 - 00;35;53;25 If our listeners want to learn more, though, about what Oracle's initiatives are or if they want to get in touch with you, is there a way for them to do that? You know, first, my thanks for having me on. I really enjoyed the conversation and pretty good and a couple tough questions in there. So thank you for that to join it. 00;35;54;04 - 00;36;20;28 Everyone is welcome to go to my page and connect with me. I try to post relevant things on occasion. So Michael from set of enforcing the Oracle dot com and find the Oracle Health Sub page of the Oracle Life Sciences of the Explosive Alexa Science Stage Armageddon Sounds good. That got it. Thanks again, Michael. And to our listeners, we don't want you to miss any episodes of research and action. 00;36;20;28 - 00;36;49;01 So please subscribe to the show. And if you want to learn more about how Oracle can accelerate your own life sciences research, you can just go to Oracle dot com slash life dash sciences and we'll see you next time.
How can patients and their families become more integral in the clinical research process? How can patient-led research become more accepted in the scientific community? How are inspiring groups forging new, collaborative paths for science and medicine, and reshaping how medical research is conducted? We will tackle those questions and much more in this episode with Amy Dockser Marcus, a Pulitzer Prize-winning journalist and author of the recently published book, “We the Scientists: How a daring team of parents and doctors forged a new path for medicine.” Amy is a veteran reporter at the Wall Street Journal and won her Pulitzer Prize for Beat Reporting in 2005 for her series of stories about cancer survivors and the social, economic, and health challenges they faced living with the disease. She has covered science and health at the Journal for years, and she also earned a Masters of Bioethics from Harvard Medical School. -------------------------------------------------------- Episode Transcript: 00;00;00;00 - 00;00;24;19 How can patients and their families become the centers of research? What is open science and who are citizen scientists? We'll explore those questions and more on this episode of Research and Action in the lead in. Hello and welcome back to Research and Action, brought to you by Oracle Life Sciences. I'm your host, Mike Stiles, and our guest is Amy. 00;00;24;19 - 00;00;48;22 Dr. Marcus That's right, that Amy Marcus, the Pulitzer Prize winning journalist, reporter at the Wall Street Journal, a Pulitzer Prize, was won for her series of stories in 2005 about cancer survivors and the social and financial challenges of living with cancer. Her beat, as you would imagine, has long been science and health. And she holds a master's of bioethics from Harvard Medical School, and she's an author. 00;00;48;22 - 00;01;04;26 Her book is We The Scientists How a Daring Team of Parents and Doctors Forged a New Path for Medicine. So this should be interesting as we talk about collaborative, open science and the rise of citizen scientists and patient led research. So thanks for being with us, Amy. 00;01;05;01 - 00;01;06;22 I'm happy to speak with you today. 00;01;06;22 - 00;01;26;29 Great to have you. In your new book, you take readers through some really, frankly, heart wrenching experiences that patients and their families have gone through with a rare and devastating disease called Niemann-pick. Hopefully I'm pronouncing that correctly. Tell us about the book and that disease and what fascinated you about this story. 00;01;27;14 - 00;02;01;21 The origin of the book really is a personal story, which is my mother got diagnosed with a rare type of cancer. And when I tried to do research on her behalf, I started to learn how challenging it is to develop drugs for rare diseases. After she passed away, I took some time off from the Journal. I had a research grant from the Robert Wood Johnson Foundation and I started traveling around the country looking to see if there were new models that might accelerate drug discovery. 00;02;01;29 - 00;02;25;21 And during the course of that research, I was introduced to a group of parents whose children have this rare and fatal genetic disorder, NIEMANN-PICK type C disease. It's a cholesterol metabolism disorder, so the cholesterol doesn't get out of the lysosome and that compartment in the cell and it starts to build up and it causes all kinds of problems. 00;02;25;21 - 00;02;52;12 And the children eventually lose the ability to walk and to talk and to feed themselves. But the parents that I met wanted to do something novel. They had found a group of scientists and researchers and clinicians and even some policymakers in the government that wanted to work together as partners and to see if they could accelerate the search for a cure or an effective therapy for an epic disease. 00;02;52;19 - 00;02;58;11 And they let me follow along during the course of that partnership for over ten years. 00;02;58;24 - 00;03;05;24 That's amazing that you got that kind of insight. And what did you learn over the course of that ten years? 00;03;06;22 - 00;03;34;15 Well, I was really interested in how they saw the production of science in a different way. They all wanted to try to save or extend the children's lives The disagreements lay in. How do you go about prioritizing drugs? What amount of risk is a patient or a patient's family willing to take compared to the level of risk that a doctor or scientist wants the patients to take? 00;03;34;15 - 00;03;54;14 These sorts of tensions arose, I think, in part because they were modeling a new method of where the patients expertise was considered as valuable or even at the center of this of this project. And that's not usually how it is. 00;03;54;14 - 00;04;09;09 But that's rare, right? I mean, in our in the culture of our health care system, it's not really common that the patients input or the patients families input is invited at all. 00;04;09;19 - 00;04;34;11 Yeah, I think that that you're right about that. I mean, the traditional way of setting things up is that the scientists devise the hypotheses and they then construct trials in conjunction with clinicians and sometimes with pharmaceutical companies, of course. But in this particular collaboration that I was describing, the drug was not in the hands of a pharmaceutical company. 00;04;34;11 - 00;04;59;06 It was widely available. And so the partnership was truly about, you know, going to be conducted at the NIH. And therefore it gave the parent and the families, I think, more leeway to do this experimental idea. What if we all recognized each other's expertise? What if we all saw each other as equal partners? What if we got to weigh in? 00;04;59;13 - 00;05;20;24 Not in once. You've already set up the clinical trial, but at the very, very outset, when you're simply going through the scientific literature to come up with potential compounds, when you're thinking about what might work, when you're trying to prioritize what to do first, second and third, all of those things where patients don't always have a voice. But in this case they really did. 00;05;21;07 - 00;05;43;16 You know, we just had Hilary Hannah Ho on the show. She's secretary general of the Research Data Alliance, and we talked about open science and open data and how important all that is to getting the scientific breakthroughs that will actually help people and get to those breakthroughs faster. But open science can kind of be polarizing. There's some confusion around what exactly it means. 00;05;43;23 - 00;05;48;14 How would you define or describe open science and citizen scientists? 00;05;48;27 - 00;06;34;22 Yeah, I think that's a really good point, that there isn't one sort of accepted name and that there are many names and people use different phrases when they're thinking about different things. For me, I used the term patient LED research and I often use the term citizen science. And what I meant by that was, again, what we've been talking about from the outset, which is a recognition that the patient, the patient experience should be at the center of everything, a recognition that the patient and the families are experts, that they have the ability not only to be beneficiaries of scientific knowledge, but also creators of scientific knowledge. 00;06;34;27 - 00;06;46;15 And to me, that shift the idea that you can be a creator of scientific knowledge is the fundamental one that needs to happen if we're going to really reach the goals that I think we all want to reach. 00;06;46;29 - 00;07;11;10 So here's something we highlighted in your book. Quoting here Science is inherently a social enterprise. Yet too often scientists operate behind closed doors, removed from the very people they intend to help. That's struck me as kind of a mike drop statement with a lot of truth to it. But did the pandemic change anything? Was the work still removed from those patients on ventilators and ICU? 00;07;11;20 - 00;07;52;04 So I do make a point in the book to draw some parallels between the various patient led research movement experiences that I describe and the COVID 19 pandemic, and in particular the group of patients that call themselves long COVID patients, where they're suffering symptoms for many, many months. I argue that COVID allowed us in real time to to recognize that anyone can be an expert and that now that is something that it was easier to see during the pandemic because there was a novel virus, there weren't established experts yet. 00;07;52;14 - 00;08;25;28 And so while doctors and scientists and the government were scrambling to try to help patients, I think they also saw themselves for the first time as part of this effort to understand the disease. Together, there wasn't already an understanding of COVID 19. And so what I say in the book is that we can draw from from that experience and sort of take that part of it forward where we say patients should be at the center of things. 00;08;26;06 - 00;09;07;01 Patients are experts. Patients are able to identify things that many scientists or doctors didn't have time to recognize because they were they had to focus on trying to save lives and, you know, working in a vacuum at that point. So there also was a sense of urgency. Like one of the things that I was struck by during the pandemic as a as a science reporter was that scientists were able to put their papers online right away on these websites before it had gone through the full peer review process because it was recognized is so essential to get this information out there as quickly as possible. 00;09;07;09 - 00;09;29;16 And everyone understood that maybe there were going to be some mistakes. It wasn't fully vetted, but it was out there. Not only was it publicly available to the doctors and scientists who are also studying it, it was publicly available to patients and people who are simply interested. And long COVID patients organized themselves, did research on themselves, and they also published their papers on these websites. 00;09;29;16 - 00;09;43;22 I think those types of models where patient researchers can be contributors and can benefit from the information to fuel their own research, I think that should move forward and is it shouldn't be just a relic of the COVID 19 pandemic. 00;09;44;07 - 00;10;05;03 But what isn't there a risk of chaos a little bit? Because we're always told, hey, whatever condition you have, don't go Googling it on the Internet. You'll just go down a rabbit hole and, you know, worry about all these conditions that you may or may not have. So what is the risk of, like you said, mistakes and wrong information being published? 00;10;05;13 - 00;10;27;11 Well, even the traditional peer review process in science publishes papers that turn out to have mistakes in them. Papers are retracted all the time. And there is a well-known phenomenon that peer reviewed papers sometimes the results can't be replicated. I mean, that's the problem for science. I don't think that's a problem just for having patient researchers get involved. 00;10;27;28 - 00;10;54;27 I also think that the advice not to Google something is both old fashioned at this point and probably unrealistic given that almost all of us are connected in some way through the Internet. My sort of idea, rather, is that let's use the Internet and other methods to become better partners. Let's share good quality information online that people have access to. 00;10;55;06 - 00;11;20;20 Let's form partnerships where we can collaborate, where among experts, the people that I was talking to and interviewing and spending time with the parents, they weren't saying, Hey, we're trying to go it alone. We know everything. No, the opposite. What they were saying is we have very relevant and valuable information. We are experts because we live with this disease and we know what level of risk we're willing to tolerate. 00;11;20;20 - 00;11;43;28 And we do our own research. But we need partners who can also help us fill the gaps where we don't have knowledge. We want to collaborate with scientists, we want to collaborate with clinicians treating our children. We want to collaborate with government scientists who have access to data and and robots and things that we're not going to have in lab equipment that we don't have access to. 00;11;44;06 - 00;12;02;19 So no one's saying, go down a rabbit hole by yourself. What people are arguing is let's find ways to pool information, and by pooling everyone's information, we can sort through more quickly what's good, what we think is good, but might turn out not to be good later. And what can benefit all of us. 00;12;03;04 - 00;12;20;02 Yeah, and from a technology standpoint, gathering that data and organizing it and working with it is becoming more possible than ever. COVID should have scared our health system out of its mind. Did it? And is that leading to any systemic changes in science and health? 00;12;20;15 - 00;12;46;19 Well, I'd like to focus on what my book was focusing on, which is can a group of patient activists and scientists and clinicians and government policymakers working together make changes to the system? And I think the answer is yes. You can make changes to the system. The patient researchers that I was talking to and the families I was talking to, they built on activist patient work that had gone before. 00;12;46;19 - 00;13;10;06 And there have been responses in the past. HIV activists were able to influence the FDA to pass the accelerated approval rule that now allows drugs to be approved more quickly. And I think that, you know, compassionate use program that FDA has the patients in my family, the patients in my book and the families benefited from that as well. 00;13;10;17 - 00;13;48;01 So there have been changes along the way. But I think what my book is arguing for, and I think this message came out of the COVID 19 pandemic as well, is that even with all the changes that have been made in the past, the patient experience is still not at the heart of the system. And I think that's the message that all of these families are saying put the patient experience at the heart of things, and then you will see that the system, when you configure the system around the patient centric experience, you'll see that it will work in a different way and an I think, a better way. 00;13;48;02 - 00;13;50;02 But we need to run that experiment. 00;13;50;17 - 00;14;12;20 So we mentioned the concept of citizen scientists. That's what we've been talking about. These are people that pursue what they pursue, driven by mostly love and urgency for their kids, which is just a whole different level of motivation than most researchers have. I think you have a few stories about, you know, people like Chris and Hugh Hempel and and some others that went through this experience. 00;14;13;02 - 00;14;34;21 I want to make a point here that I think also is really important for people to understand who are listening to this. The parents in my book and you know, you cited Chris and Hugh, they were definitely among the pioneers who did this. And there was Phil and Andrea Morella, and there were also Darrel and Mark Poppea who are who are part of this, too. 00;14;34;21 - 00;14;57;29 And many, many other parents. I mean, the Parseghian Research Foundation and the National Niemann-pick Disease Foundation, all family driven. The people who are doing this. Yes, they are driven by their love of their children. They are driven by a sense of urgency. But they're not going to the FDA and saying, Hey, please pass and approve a drug because we love our children. 00;14;58;05 - 00;15;24;05 Please pass and approve a drug based on our emotion. No, not at all. They want to give effective drugs to their children. What they are saying is we are creating scientific knowledge and we think that that should be part of this approval process, that should be part of the drug development process. I just want to give some examples that I cite in the book where the parents were creators of scientific knowledge. 00;15;24;24 - 00;16;07;11 You had parents who read the scientific literature, published scientific literature, called up. The scientists interviewed the scientists came up with hypotheses themselves that they proposed to scientists, contributed to the two scientific experiments, coauthored papers that were published in the peer reviewed scientific literature. You know, went to the NIH regularly to have meetings where they helped contribute to assessing and prioritizing which compounds should go first in terms of advancing them into clinical trials, contributed their thoughts on the risk benefit analysis in devising the clinical trials. 00;16;07;22 - 00;16;34;28 One of the parents went to an FDA sponsored workshop for how to file an orphan drug designation, which is part of the approval process and the long process to getting approval for rare disease drugs. And went to the workshop, participated in the workshop, presented scientific data to the regulators, met with the regulators, and earned an orphan drug designation for one of the compound Cyclodextrin that got moved forward. 00;16;35;07 - 00;16;46;24 So yeah, they have a sense of urgency and yes, they love their children and want to save their lives, but they're producing real scientific knowledge and I really hope that that people take that message away from reading the book. 00;16;47;10 - 00;17;08;15 So those are great examples of exactly what citizen scientists do that sets them apart from just patients who are not doing that level of research, that depth of research. You talk about Chris Austin and the book, and I'm going to read another quick excerpt here, The Promise of Genetics to Deliver new interventions, new drugs and new treatments for patients is not going to happen. 00;17;08;15 - 00;17;27;28 Chris told his boss, unless there's some way to get through the valley of death. Francis gave Chris a green light to pursue his vision. So the boss in that excerpt is former National Institutes of Health director Francis Collins. What is the Valley of Death and Chris's role in citizen led research? 00;17;28;06 - 00;17;54;21 Great. No, that's a great question. So Chris Austin is a Harvard Medical School trained neurologist, also with a background in genetics who worked at pharmaceutical companies as well, and then found his way to the niche where he worked for Dr. Collins and became also a director of one of the institutes at NIH called Ed Katz, the National Center for Advancing Translational Science. 00;17;55;06 - 00;18;23;29 And one of the sort of green lights he got from Dr. Collins was to set up a lab that would have robots that were sort of at the same type of robots that pharmaceutical companies have that would work around the clock and could rapidly screen drugs to try to find compounds that might work for diseases. And what Chris Austin's idea was is that let's screen these vast libraries. 00;18;24;04 - 00;18;50;06 Let's find some drugs that might be promising, and let's also find patient partners. Let's find scientist partners, and let's then try to take all this data and move it forward together. One of the hypotheses that Chris Austin said he had as a scientist was can drug development go faster if patients and families are part of that process from the very beginning? 00;18;50;18 - 00;19;17;02 And one of the things that Chris Austin was trying to get around is this valley of death, which is this, you know, where compounds kind of go to die. You have a great idea as a scientist. But how do you get that idea from the bench to the clinic and to a patient's bedside? And the Valley of Death is just all the various obstacles that end up making it hard to develop a drug. 00;19;17;13 - 00;19;39;21 Some of it can be scientific. You know, you test it in a in a mouse or an animal, you test it in the lab and it turns out to be toxic for the cells or the amount of drug that you need to give to a person is so high it's not realistic or a drug company decides they want they don't want to put any money into it anymore or it gets or a drug company gets bought and they don't want to pursue it anymore. 00;19;39;21 - 00;20;02;08 And there's a million things that happen in the Valley of Death. But Chris Austin's vision was if we can involve patients and families as partners, along with scientists and drug developers and government officials from the beginning, maybe we can get things out of the Valley of Death, or maybe we can fail faster and find the successful compounds more quickly. 00;20;02;25 - 00;20;22;23 Yeah, a big takeaway from your book is the need to build bridges between science and citizens. But and we talked touched on this a little bit. You can't sacrifice scientific rigor or safety. So what are the challenges to building these bridges? What's holding that process back, especially when it does come to drug discovery and clinical trials? 00;20;23;09 - 00;20;47;05 So I think that there is a variety of issues that make it challenging to build bridges. For one thing, there's often a tension between, you know, people who are sick or are advocating on behalf of people who are sick, who really want to focus on the here and now. They they really need something to help their loved one right now. 00;20;47;19 - 00;21;22;19 And often, you know, clinical trials are an experiment. And when you enroll in a clinical trial, you're told this is not designed for the benefit of you. This is designed to benefit future patients. And therefore, it's not a treatment and it's not the equivalent of clinical care. And that can be a source of frustration and tension. And often also when research crews are doing research, they weigh the risk benefit assessment of moving drugs forward differently than people who are trying to you know, solve a problem now. 00;21;23;00 - 00;21;48;14 So I think that and that came up in this partnership in my book. It came up in this partnership in my book a lot. And yet I think each side was able to get a sense of what the points were, what the what the tensions were. But again, in my opinion, one of the ways that they overcame this divide was by both sides saying patient centric medicine is the way to go. 00;21;48;15 - 00;22;16;29 Patient centric science is the way to go. There are ways to collect data in a rigorous manner that can both benefit patients now and also not stop you from insights that will lead to benefits in the future. There are ways to come to terms with that. Some people have a higher acceptance of risk than others. I mean, we see movement towards that already right now. 00;22;17;01 - 00;22;23;01 I think that one of the messages of my book is to try to accelerate that even further. 00;22;23;25 - 00;22;37;19 Well, to that point, you say in the book, government and agencies like the FDA and NIH have a vested interest in helping these science and citizen partnerships succeed. Do they understand that? And what role should government be playing to move this forward? 00;22;38;01 - 00;22;57;01 Well, government is not one person. You know, so but I think that the book shows that there are people in the government who were partners with the patients and the families and the scientists and the clinicians. I mean, this whole book is about a partnership. And Chris Austin, although he's no longer in the government, he left the government. 00;22;57;10 - 00;23;28;05 He was in the government at the time, and he was a partner with these people. So I think that the government has shown in the book that, you know, and outside of my book, obviously interest in investing in new ways to do science, interest in investing in new ways to accelerate science, the government is supposed to represent the interests of the people, and the people's interest is in being healthy and in and trying to find solutions for drugs. 00;23;28;14 - 00;23;56;15 So in the book, I do talk about how the patients and the families in my book were able to directly talk to FDA regulators. Some of the parents went to workshops that the FDA was sponsoring. They had conversations with FDA regulators. I think those types of workshops are really novel and they really are fruitful because they allow the families and the patients to really think like scientists and to produce science as they can and should do. 00;23;56;16 - 00;24;21;16 They want to produce science. And I think also one of the messages that Chris Austin gave at representing the NIH was that the NIH is here to be your partner, and we're open to coming up with novel ways of accelerating science. So I think that there's there's openness to doing this, but of course, always more can be done. 00;24;21;17 - 00;24;51;16 I mean, patients have a sense of urgency, and that's the message that they bring to the government all the time. I mean, in the book, I, I describe FDA advisory committee hearings that are held when the FDA isn't sure about the data and they want to have a public hearing about it. And many of the parents and families showed up and gave testimony not just about their thoughts and their opinions, but about the data that they had gathered, the science that they were generating, that they wanted to share with the FDA and be heard. 00;24;52;00 - 00;25;16;13 What role does Rules Framework's guidelines play and what we're talking about here? I think you even your former advisor, was part of a group of scientists that worked on this framework. And the platform for patient led research, I think was spearheaded by that advisor, former advisor and a group of scientists. What's the infrastructure that needs to be put in place for this to work? 00;25;17;08 - 00;25;46;03 So, yes, So the advisor that you were referring to, Effie Diana was my advisor in my bioethics program and she does a lot of pioneering research on patient led research movements. And she and a group of collaborators, scientists and, and social scientists and clinicians and, and policymakers got together and tried to devise what they called a new social contract. 00;25;46;13 - 00;26;14;17 What they argued is, is that patient led research is a novel form of research that doesn't fit into the traditional regulatory standards that have guided, you know, clinical trials and human subjects research up until now. And that's because the traditional methods of regulation are based on the idea that scientists are going to be leading the research and doctors are going to be leading the research. 00;26;14;26 - 00;26;42;04 And that still is the traditional model. And they usually are leading the research. And in those cases, they often have more information and more power than the traditional patient or human subject. So Effie and her collaborators weren't arguing. We're arguing that the traditional rules should be thrown out because obviously patients do need protection and human subject research does need regulatory guidance. 00;26;42;11 - 00;27;17;26 But what she and the others were saying is let's also think about these new ways of doing research and how we can get scientists and clinicians to accept the results. That patient led research arrives at. And one of the ways she and the others said is let's come up with ways that patient researchers can seek ethical guidance. Let's put tools online that they can use so that they can devise experiments in ways that approach the rigor that traditional scientific experience experiments do. 00;27;18;06 - 00;27;52;04 Let's generate research that's of benefit to the people now, but also can be useful in guiding treatments in the future. Let's make a path towards publishing their data in peer reviewed journals. Let's make them part of the peer review process. I mean, you do have journals now that have patient researchers participating in peer review of scientific papers. And you have groups like Pachauri that ask scientists and patients to collaborate together on experiments. 00;27;52;13 - 00;28;24;24 So I think I think what she and the others were getting at is the current contract that we have may still be fine in certain circumstances, but isn't set up to address this new kind of research that's being done. And if we want it to be generalizable, scientific knowledge, which is always the gold standard, then we need to work together to help all of the partners to do better research that meets the standards that we can all except. 00;28;25;09 - 00;28;40;27 When you kind of make the promise of patient led research obvious. But, you know, how many times do we see things with great promise get tied up in knots? Is a paradigm shift likely? And if so, how long of a runway is that going to need? 00;28;41;15 - 00;29;01;11 I mean, I don't know how long it's going to take, but if there is a message in my book, if there is a message from the people that I focused on in my book, I mean, they've been working together for more than ten years. They've made a lot of progress, but they're not where they want to be yet. 00;29;01;20 - 00;29;23;29 So that's a long time. And I think that they want to go faster. I think the message of long COVID patients is we need to go faster. I think the message of HIV activists and breast cancer activists and disability activists is we need to go faster. And I don't think that you need to change a paradigm in a day. 00;29;24;12 - 00;29;53;19 Paradigms, by definition, take time to change, and they involve a lot of debate and discussion, dissension. And that's what happens in a society. People have different, different views. But I think what we're getting at here as a society is that patients need to be at the center of any paradigm that exists and that if everyone works together towards that goal, they may not agree how to get to that. 00;29;53;24 - 00;30;14;21 They may have different ideas on how to ensure that the science is rigorous and works. But if they keep this notion always at the center that the purpose is, is patient centered science, then I do think that you can end up with a paradigm that works better for more people. 00;30;15;16 - 00;30;27;10 One of the chapters in your book is Cathedral of Science, and in it a professor at Harvard. Had you read the story Cathedral by Raymond Carver. Why did they have you read that? And how does that relate to what we've been talking about? 00;30;28;04 - 00;30;55;26 Yeah, I mean, I say in the book that when we were told to read Cathedral by Raymond Carver, I was really surprised because usually in in my bioethics classes when we talk about stories and narrative bioethics, many of them involve sort of cases drawn from real life and cathedrals, really a quiet story that involves a married couple that seems to be drifting apart. 00;30;56;06 - 00;31;16;24 And the wife invites a friend who happens to be a blind man to come and stay with her and her husband. And the husband's a little bit jealous of the relationship that this person has with his wife and he doesn't really know what to say to him. And the wife goes to sleep and leaves these two men alone watching TV together. 00;31;17;00 - 00;31;38;19 And they start to watch a program about the building of a cathedral. And the narrator says to the blind man, Have you ever seen a cathedral? Do you know how to build a cathedral? And the blind man says, Let's draw one together. And the two of them construct a cathedral together. The man places his hand on the husband's hand, and they draw that cathedral. 00;31;38;27 - 00;32;01;23 And at the end of creating this cathedral, it's the blind man who says, Let's put some people inside, inside the cathedral. What's a cathedral without people? And I thought about this story all the time as I was spending time with the families and the scientists, because so many of the scientists were products of the Cathy trial of science. 00;32;01;23 - 00;32;34;13 They were the products of the best medical schools. They worked at the NIH. They I mean, they they really were, you know, part of this edifice that's been constructed and that has benefited so many people. And one of the things I kept thinking about is how do we put more people in this cathedral? I mean, that's really one of the messages that came through in this partnership that the parents and families and scientists and doctors and government officials were constructing a cathedral without people isn't really what you're looking for. 00;32;34;20 - 00;32;52;05 You're you're looking to use the power of science and research to help people. That's should be the goal of everything. And that's really the message I took from this story, that it touched me in just such a fundamental way. And it wasn't even a story about science. 00;32;53;27 - 00;32;57;18 As literature often does. That inspires us in many different ways. 00;32;57;21 - 00;32;58;20 Absolutely. 00;32;58;27 - 00;33;20;02 What did I miss? I mean, what is it that our listeners should know that you cover in the book that's important for them to know or some way that they can help or participate in this kind of effort? Or is there something that a follow up book might cover, something that you think needs additional exploration? 00;33;20;11 - 00;33;53;25 Well, I mean, I think that the message of the book is that we can all be scientists, right? I mean, it's in the title. We, the scientists, and I chose a title that echoes We the People, because I wanted people to think about the fact that what works best is a partnership. What works best is when we all come together and try to bring our different visions forward and to come up with something that will benefit all of us. 00;33;54;07 - 00;34;15;25 I think, you know, one of the things that I was struck by during during the research, not only for this book, but also when I, you know, covering health and science as a reporter is that all of us really are patients. We're either patients now or we were in the past or we will be in the future, or we love people who are patients. 00;34;16;04 - 00;34;50;28 We're advocates for those people, even if we're a doctor or a scientist, we're often on the other side of the table either trying to advocate for people we love or because we're patients. And so I think we all have a vested interest in creating a system that works well for all of us that remembers that we need treatments and that we that we need science and that all of us are experts in our own lives and that we can do research in a way that can contribute to advancing health and wellness for us all. 00;34;50;29 - 00;34;56;00 So I feel like that's the message that I hope is the takeaway of the book. 00;34;56;12 - 00;35;03;10 Well, I'm pretty sure there are listeners who are interested in the book and getting it or getting in touch with you. How can they do that? 00;35;04;00 - 00;35;26;00 So there are a variety of ways to get in touch with me. My email is publicly available. It's Amy Marcus at WSJ dot com. I'm on Twitter at Amy D Marcus. You can go into the bookstore and get the book, you know, in person, or you can order it online. You can get it from bookshop. You can get it from Powells. 00;35;26;00 - 00;35;32;18 You can get it from Amazon, Barnes and Noble. I mean, they're, you know, any, any, any place online. You can order the book. 00;35;32;26 - 00;36;03;05 Great. We appreciate that. And we want to thank you for being faithful listeners to Oracle Life Sciences, Research and Action. As always, we invite you to subscribe so you don't miss a single episode. And also maybe tell your friends and colleagues about the show as well. And we'll be back next time with more research and action.
How can an extensive collection of real-world data help find more diverse and better participants for clinical trials? How do we create a continuously learning ecosystem that helps bridge the gap between clinical research and clinical care? And what are the biggest challenges to patient record standardization and personalized healthcare? We will learn that and more in this episode with Dr. Lu de Souza, Vice President and Executive Medical Officer of the Learning Health Network, which is a division of Oracle. Dr. de Souza leads a team that seeks to help health organizations integrate clinical research into everyday care. That means addressing clinical discovery cost, time, and patient inequities. She's also a huge advocate for real-world data and bringing technology to bear for true healthcare advancements. Dr. de Souza has years of experience in health informatics and was the most recent CMO of Cerner in North America. She practiced pediatric hospital and emergency medicine until 2020 and has held multiple leadership and teaching positions. -------------------------------------------------------- Episode Transcript: 00;00;00;01 - 00;00;25;21 How can an extensive collection of real-world data help find diverse participants for clinical trials? Are some organizations already using the concepts of a continuously learning ecosystem. And what are the biggest remaining challenges to patient record standardization and personalized health care? We'll find all that out and more on today's Research in Action episode. 00;00;27;05 - 00;00;47;23 Hello and welcome to Research in Action, brought to you by Oracle Life Sciences. I'm Mike Stiles and our guest today is Dr. Lu de Souza, vice president and executive medical officer of the Learning Health Network, which is a division of Oracle Life Sciences. In a nutshell, Dr. de Souza leads a team that seeks to help health organizations integrate clinical research into everyday care. 00;00;48;03 - 00;01;11;28 That means addressing clinical discovery, cost time and patient inequities. She's also a huge advocate for real-world data, bringing technology to bear for true healthcare advancements. Dr. de Souza has years of experience in health informatics and was the most recent CMO of Cerner in North America. She practiced pediatric hospital and emergency medicine until 2020 and has held multiple leadership and teaching positions. 00;01;12;12 - 00;01;16;03 Dr. de 'Souza, thank you so much for taking the time to be our guest today. 00;01;16;14 - 00;01;20;12 Now Thank you, Mike. It's really a pleasure to be here. And please feel free to call me Lu. 00;01;21;02 - 00;01;29;21 There's a lot of ground to cover here. But first, let's just find out about you. What was the life path that brought you to where you are today and doing what you're doing today? 00;01;30;15 - 00;01;55;05 You know, as you mentioned, I am a pediatrician who focused on taking care of sick kids in the hospital and the emergency department. And I really loved my job. But like many doctors, I felt frustrated by the inefficiencies of health care. And I felt very frustrated with the limitations of time and data that we suffer both of those things are super essential to make the fast decisions that we need to make. 00;01;55;23 - 00;02;16;20 So I started thinking about technology and the role that it could play in solving some of these foundational issues. And also, you know, we always want to see how many more patients we can help. So I felt like the pivot would allow me to take care of patients in a different way, but at higher numbers. It was not easy decision. 00;02;16;20 - 00;02;41;20 It was very hard for me to leave full time pediatrics, so much so that I stubbornly continue to practice for the first ten years that I was full time at Cerner. But at the time that I was considering joining Cerner, my mother's breast cancer was misdiagnosed and that happened because of inequities, fragmentation in care and a lack of standardization that exists today. 00;02;42;00 - 00;03;08;03 Eventually, she turned out okay with that. But these missteps and delays in diagnosis led to a much more aggressive course of treatment and the complications that came with it. But this experience really sealed the deal for me. I felt like there was a lot of work that I could contribute to so that led me to my career in informatics that started with EMR implementations and technology enabled process improvement. 00;03;08;28 - 00;03;30;25 Then ten years later, my cancer warrior mom was diagnosed with a different cancer. This one was rather rare and aggressive, and we quickly found that there was not enough research to support any specific type of treatment for her and that the survival rate for anything that they could try was pretty low. And that was not good enough for her. 00;03;31;07 - 00;03;57;05 She decided to forego treatment and instead focus on having better quality of life for the remainder of the year that she was with us all of nine months. In stories like that, Mike, are super common. Many of our listeners, I'm sure, have gone through something like it and as devastating as it is, these life experiences also help shape us and they bring these opportunities that we hadn't considered. 00;03;57;19 - 00;04;25;17 And sure enough, only a few months after her passing, the Learning Health Network was founded and I was asked to help out and I was immediately drawn to its mission and vision and the impact that it could have in cases like my mom's. So it took a little bit of time to get here. But last year I was able to take on a full time role with Learning Health Network, and I'm just super excited to be a part of this awesome team that brings transformation to research. 00;04;26;07 - 00;04;29;03 Okay. And tell us what the Learning Health Network is. 00;04;29;09 - 00;05;01;06 All right. So I'm going to start with the why and why it was created and paint this picture for for everyone to understand how important this is today. Clinical discovery. So how we get to medicines and treatments and different diagnostics is still a major challenge for life sciences and health care organizations. And because these two sectors of our industry are mostly siloed from one another, it's a very onerous process for patients and providers to participate in clinical trials. 00;05;02;01 - 00;05;27;13 Even myself as a doctor who understands the language of medicine had a really hard time finding out what types of trials were available to my mom, just as an example. So for context here, when we're bringing a new drug to market, it takes approximately 17 years and it costs an average of $2.5 billion. That those are crazy numbers, right? 00;05;27;22 - 00;05;59;13 And the biggest driver of that time and cost is getting to the patients, identifying the right patients, recruiting them and enrolling them into these trials. And about 20% of these clinical trials fail because they cannot recruit enough patients. And overall, only 3% of our population participates in these studies. Of course, 3% of the population cannot be representative of the diversity that we have here in United States or across the globe. 00;06;00;02 - 00;06;30;04 So the Learning Health Network was created to help solve these problems with the concept of these patients are in everyday care, and that's where trials need to go. We need to bring research into everyday practice. The Learning Health Support Network is a partnership between Oracle and health systems that we serve, and these organizations contribute their de-identified data to serve as the fuel for research and clinical discovery. 00;06;30;18 - 00;06;59;09 So this data set is called the Oracle Real World Data, and I'll call it our RWD from now on to to make it easier. And it's one of the largest datasets in the world like this in exchange for that data contribution, which we're immensely grateful for, Oracle provides these organizations the access to the data set so that they conduct they can conduct their own research, and we provide that at no cost. 00;06;59;21 - 00;07;22;05 We also do all of the heavy lifting for them, so it doesn't take any effort on their side to get the data there to make it de-identified and normalized. We do all of that work and then we offer a variety of benefits for them depending on where they are in the course of doing research, whether it's data science or clinical trials and so on. 00;07;22;22 - 00;07;58;05 So the Oracle Real World Data is home of about 108 million active longitudinal records from all over the United States, covering about 2600 facilities. And this membership comes from a variety of organizations. These whole systems can be large, multistate and academic centers all the way down to critical access hospitals. And this combination, this this composition of membership is intentionally done and balanced by us. 00;07;58;05 - 00;08;37;11 So they're very similar in numbers. And that becomes our superpower by having data from such a wide range of facilities and such diverse communities, and means that people who never had access to clinical research near their homes can now be represented in this dataset and represented in a lot of research that gets done. And it also means that this research, a big data set, matches fairly well to the US Census and brings that much needed diversity that we're lacking in clinical trials today, and that helps decrease the the health and research inequities. 00;08;38;01 - 00;09;03;26 How we do this is again, by using the dataset to find the patients. So we find patients that are good matches for trials, and then we find trials that are good matches for those sites and for that community. And the data can also be leveraged like I said before, by organizations to drive or derive clinical insights by using data science and the tools that Oracle provides. 00;09;03;26 - 00;09;05;10 That is us in a nutshell. 00;09;05;28 - 00;09;28;17 I think there's a lot of people listening that would be really surprised to find out the thing that slows down getting new drugs and new treatments to market isn't necessarily like bureaucracy or red tape or lack of scientific knowledge. I think people would be surprised to find out the real problem is being able to find and get people and a diverse group of people to participate in these clinical trials. 00;09;28;17 - 00;09;32;09 So that's probably what adds great value to this dataset, right? 00;09;32;29 - 00;09;54;27 Yeah, I mean, the things that you mentioned definitely are barriers that we have to cross as well. But it was surprising to me as well as I entered into this space. Just as an aside. One of the reasons it's so important for clinical research to be embedded into care is because we people, patients, we trust our health care providers. 00;09;55;10 - 00;10;09;15 You know, these are the people that we listen to and take advice from. So the studies have shown that the majority of patients that enter clinical trials or accept to participate are because those trials were discussed by their providers. 00;10;10;05 - 00;10;15;00 And what's your role in it? What what constitutes a really good week or a month for you? 00;10;15;15 - 00;10;47;21 As the executive medical director, my main responsibility is really to the health system. Members. I have a team, a super awesome team of clinical researchers that ensures these members gain value from their incredible data contribution and also know how to leverage it. We provide programing around them so that they can learn, collaborate, network and so on, and I also lead our clinical research strategy and operations, which is focused on two major components. 00;10;48;03 - 00;11;26;12 One is bringing the funded research opportunities to the members that want to have clinical research research programs, funded opportunities, meaning they come from life sciences organizations and cross, and also helping these organizations that are smaller to become research ready. So these are organizations that don't today have a program or are beginning and they need more support. The second major focus is breaking down the silos that exist today between clinical research and care delivery, and that will help drive the awareness, the efficiencies, the safety. 00;11;26;21 - 00;11;46;12 It will help us improve that patient recruitment into trials and so on. Now, boy, my my day to day changes quite a bit. So a good week or a month is hard to describe, but I would tell you that a really good day is when one of our community, Rural Health Hospitals, is awarded a study that we facilitated. 00;11;46;23 - 00;12;10;29 And because we know that those patients will be represented, that community will be represented in research and they will gain access to cutting edge medical interventions. It feels really good to know that we played a part in that and another really good day is also when our members use this data set to gain insights that lead to positive patient outcomes and that we're blessed to hear about that quite often. 00;12;11;01 - 00;12;19;04 Our Learning Health Network members have published over 500 peer review articles using this data set. 00;12;19;17 - 00;12;32;11 Best case scenario if the Learning Health Network gets its job right, how can that change how health care data, The gathering and use of real world data is used to improve patient outcomes and health care policy? 00;12;32;23 - 00;13;19;26 Yeah, I would just reiterate a couple of things. With the Learning Health Network and its real world data, we'll have real data in real time deriving insights to lead to better care and better outcomes in the continuously learning ecosystem. We'll be able to quickly restudy and improve upon those longstanding medical practices we have today. So the word restudy is really important because we do have a lot of medical practices today that are gold standard and they're based on old research or based on research that didn't include certain populations, didn't include the necessary diversity or, you know, certainly the composition of us as human beings has changed. 00;13;19;26 - 00;13;43;22 So it is very important to ensure that we're still providing the best care and we can use the data for that. And that also will decrease these existing disparities and drive us closer to personalized care. The future also would look like we no longer will take so many years to complete clinical trials because we're going to know where the patients are for specific studies. 00;13;44;01 - 00;14;11;18 We're all going to know what those studies are more important to take to specific communities and patient populations. And and I think that is going to alleviate a lot of that, not just the time, but also the cost, because these costs are, you know, also what driving the cost of medications for our patients or interventions. Let's see, we'll be able to get to a more predictive and prescriptive models of care. 00;14;12;04 - 00;14;37;24 So understanding not just what happens with an individual now and how to take care of that problem, but also understanding what's likely to happen to Mike based on data points that we have on you today and behaviors. And this way we're able to intervene in the product in a proactive way. Imagine being able to predict and prevent a heart attack from happening three years from now. 00;14;38;05 - 00;15;10;24 All of these things are in our reach today. And the good news is that we're not too far from them. In fact, our our member organizations, the ones that are using the the real world data, are already experiencing practice and research transformation. But we certainly need to scale this up, scale this approach, and hopefully we'll get to a point in which the medical community will trust more on approaching research in this way and it becomes more the standard of care of how we discover and apply changes. 00;15;11;11 - 00;15;18;04 And I also think there is going to be a lot of other possibilities of this data set brings that we haven't necessarily conceptualized yet. 00;15;18;23 - 00;15;23;23 So follow up question You mentioned that organizations are already doing this. Can you give us an example or two? 00;15;24;19 - 00;15;50;12 Sure, sure. I'll give you two of my favorite examples, not just because I'm a pediatrician, but also because less than 20% of all U.S. research funding is dedicated to children. This is a highly underrepresented population in research, just by sheer numbers, which means that patient recruitment in trials is even harder. And conducting those trials in the traditional way is much more challenging. 00;15;50;28 - 00;16;24;20 So these two examples come from very proliferates users of real world data. And in these are pediatric hospitals. The first example comes from children's health of Orange County in California, where they have used RWD and machine learning to create what is the first published pediatric readmissions algorithm. So it's an algorithm that gives us a risk of readmissions for patients that were in the hospital or presented to the hospital, and they were able to accomplish that in the matter of months. 00;16;25;03 - 00;16;51;14 They then incorporated this risk score into the clinical workflows. They put it right inside of their Oracle, Cerner EMR, and they saw a 10% decrease in readmissions in the first two years, which is just commendable. You know, it doesn't just improve the quality of of these kids, but in today's healthcare, this change also amounted to $2.7 million in cost avoidance. 00;16;51;28 - 00;17;18;23 Everyone knows how expensive it is for hospitals when a patient is readmitted. The other example is Children's Mercy Hospital. Their research team leverages the rural data for a lot of projects, and this one is really near and dear to me because I worked in the E.R. with children. They looked at adolescents with migraine headaches that were presenting to the emergency department with these headaches and how they were being treated. 00;17;19;03 - 00;17;44;29 And what they found is that 23% of these kids across 180 AEDs were receiving opioids. I want to repeat that because that's really important to us. 23% of these children were repeating were receiving opioids as the first line of treatment, and that is not necessarily the best treatment for them. It is a misuse of the medication. And it's very aggressive. 00;17;44;29 - 00;18;23;20 And, you know, we're having already opioid crisis in this country. So then they they took that learning. They created a new clinical protocol and a clinical decision support tool that they incorporated into their Oracle, Cerner EMR, and were able to decrease the use of opioids for this condition to almost zero in their emergency departments. They had several in Kansas and Kansas City, Missouri, and just like, you know, a true learning health network, they they took this knowledge and the new clinical protocol and they presented that at headache conferences around the country. 00;18;23;20 - 00;18;39;13 And they know and and they're helping improve care for kids everywhere. So as you can see, the Learning Health Network is really a game changer for these organizations. They're now able to do research in a fraction of what it would be a typical research time. 00;18;40;01 - 00;19;07;20 That's really exciting and inspiring because you listen to every opioid addiction horror story and they all start out with an accident or a headache or a quote unquote, legitimate use for opioids that then turned into something worse later. So that's a particularly incredible impact you're having, but I'm assuming it's not that easy. So what are the biggest challenges to making the dreams you just outlined come true for society? 00;19;07;27 - 00;19;35;27 Yeah, you're absolutely right. We come across many barriers. But the cool thing about this team is we we don't find them discouraging. We're truly motivated to look for solutions in innovative ways, and we find partners that can help us as well. One of the biggest challenges of community based research is the lack of resources and infrastructure today that would allow these providers to offer trials and to conduct trials as a care option for their patients. 00;19;37;03 - 00;20;13;25 You have heard this in many other ways from other people of just how burned out providers and clinicians nurses are today. They're overwhelmed by the numbers. They don't have the time and support to then take on something else like research. So we try to overcome that in a few ways. Obviously, as a software company, we're continuously looking for ways that technology can support these gaps, but we also work with outside partners who can provide the actual resources or boots on the ground and expertise for these community providers to do research. 00;20;14;15 - 00;21;01;03 Another challenge is on the data and technology side, and that is that big data requires significant compute power, know it needs specialized tools, and you need specialized training. So it all can sound easy, but it's not easy. Fortunately, Oracle is the leading provider of cloud infrastructure and services. This continuous pursuit that we have for autonomous databases and low or no code applications, I always struggle with saying that these tools, it really lends itself nicely to the work that we're doing with RWD and I think it's going to allow us to challenge the market with the new generations of these data sets and tools. 00;21;02;00 - 00;21;38;13 And then lastly, I want to touch on on cybersecurity, because that is a constant challenge across healthcare and obviously our entire business is data. So we have to be very aware and cognizant and careful of it and again, I think the unique to Oracle is this ability to leverage other data security experiences that Oracle has. So, you know, Oracle has been protecting the data of the financial and banking sectors for many years, and we're able to leverage that and bring that into Oracle Life Sciences as well. 00;21;38;23 - 00;21;46;21 It's it's a level of security and governance to healthcare data that, you know, is really important to have and it feels good to have it. 00;21;47;08 - 00;22;02;10 Well, none of this happens without tech knowledge is that have come onto the scene. So first, let's talk about how far we've come. What is today's state of electronic health records and data analytics where patient care and health care delivery are concerned? 00;22;03;04 - 00;22;28;24 Yes, this is every doctors favorite subject to the notorious electronic health record in the life that I've that I've led for the last 12 years. You know, my as much as the patient records are still fragmented and EMR is are still considered clunky tools, I do think it's important to recognize the progress that we've made and the effect that it's had for us as a society. 00;22;29;08 - 00;22;54;04 You know, most people's records are digitized today. You know, there are many children that are born across the world that will never have a paper record, will have their entire record available electronically. And that means that their data is available to us and it gives us this ability to understand health care like we've never had before. But of course, our industry is challenged. 00;22;54;15 - 00;23;19;25 We still suffer from a lack of standardization in various areas and that makes data extraction and its use challenging in various ways. The way that I think about it, the simplistic way I think about it, is that old ATM cards, you know, remember how they only function in a specific bank and then years later you could use them within a network as long as you went to that particular symbol in the back of your card. 00;23;20;13 - 00;23;39;19 And then now here we are being able to access our banking information and our money everywhere in the world. And when you are anywhere and you swipe that credit card, the transaction is seamless. I mean, it's seconds there and they're doing a lot with those seconds. You know, they're checking, do you have the right funds? Are you the right person? 00;23;39;20 - 00;24;03;22 Because, you know, could this be fraud and then authorize that? So it's very impressive, their journey. And I'm sure that getting there was not easy nor fast. So similar to that. Our struggles with patient records are similar, but we've made good strides in interoperability. I think that right now we have the right direction and the right tools to get there. 00;24;04;13 - 00;24;33;29 And also, you know, we have the experience from from from these other industries that will accelerate our progress. I think, you know, one of the things that impressed me when we joined Oracle is the number of the number and the variety of industries that this company supports and partners with. And I've seen this constant pursuit of working across the verticals, looking for opportunities to learn and collaborate and understanding that we're better, faster together. 00;24;34;09 - 00;24;57;01 That's really important for us in health care because we do have this reputation of wanting to work alone and being difficult to work with. But, you know, when you look back over time, I don't think that we would be as well positioned as we are today with patient safety, for instance, if we hadn't leveraged, you know, the learnings and the experience of the aeronautics industry. 00;24;57;01 - 00;25;09;19 Right? So flight safety and those concepts were applied to to medical safety, and that's really propelled us ahead. And so I'm looking forward to continue to work across these different industries. 00;25;10;02 - 00;25;34;16 Yeah. You know, when I've asked other guests who are engaged in clinical research and recruiting for clinical research, one of the things they seem least impressed with is how spread out varied, disconnected patient records are. What's the ideal state, and can existing tech get us there, or do we need something more or is it more of a policy and bureaucracy problem? 00;25;34;16 - 00;26;10;03 I think the answer is yes. You know, expanding a little bit more on that fragmentation of of record of patient record health it's still, like I said, struggles with standardization and that's the piping and the backbone that supports good technology. So we're talking about standards for health data elements, meaning having the same names, the same codes, the same ontologies, and also standards for quality in health care data is still not universal, which is, which is a big challenge. 00;26;10;04 - 00;26;46;15 So I have this colleague that works in data quality and runs a company in data quality, and he always says, you know, garbage in means garbage out. So when data is not captured appropriately, it's output is harder to use. Another big challenge is getting to a single longitudinal health record, because we do in this country suffer from a lack of a universal patient ID So interoperability is extremely important, but it's still, you know, having some difficulties getting there to a seamless in a seamless way. 00;26;46;26 - 00;27;07;15 But once again, we have made a lot of progress. You know, I think that we're going to be in the place where, you know, you walk into any facility and you can scan your card or maybe you're going to have a chip on your on your arm there. Mike, I don't know. And those health care workers are going to know who you are and they're going to know how to take care of you. 00;27;07;24 - 00;27;32;22 So I do believe that we are going to get there on the policy side and, well, both research and health delivery are super highly regulated and rightfully so. We want them to be, but they're not always congruent. And there's definitely increased recognition that some of the policies, regulations that we have in place are outdated. We have evolved since then and they need to be reconsidered. 00;27;32;22 - 00;27;59;08 And we're seeing movement across federal sectors, like in I like the NIH and the White House to try to help some of these regulatory burdens. So we absolutely fully believe that your observations are right, and this is a great opportunity for us to help break down those those issues. And to me, that's one of the most exciting ways that we can make an impact. 00;28;00;06 - 00;28;21;18 You know, we've talked to several guests over past episodes about personalized medicine. Obviously, we don't get anywhere near personalized medicine without real world data. What are your thoughts about what the true barriers are to personalized medicine? Can we start looking for it and getting excited about it? Or are we still like a Star Trek distance away from it becoming reality? 00;28;22;09 - 00;28;32;15 Well, I funny that you mention Star Trek because I am a big fan and I still do. I still want to be Dr. McCoy with a tricorder. One of these days. 00;28;32;15 - 00;28;35;11 I think we all have dreams. 00;28;35;11 - 00;29;03;17 I always felt that watching sci fi movies is is a great way to imagine what the future can look like, like Judge Dredd and the Flying cars, you know, other industries already applying intelligence and suggestions. There are way ahead of us and these suggestions are derived everyday from everyday interactions right? You are constantly bombarded by ads that relate to a conversation you had with your spouse near a smart home device or via email or a search that you did. 00;29;04;04 - 00;29;32;26 So all of this is possible. It's very personalized, but health care data needs to be very protected. So I do believe we should be able to get there to more general personalized care, and the data is the foundation for that. There are definitely sectors or treatment areas like oncology, immunology, where these advances are already there in place. And we know more about genomics and other omics and we know how to target treatments for those patients. 00;29;33;07 - 00;29;34;29 So we are we're definitely getting there. 00;29;35;10 - 00;29;46;22 And thinking just about the Learning Health Network What do you see as the biggest opportunities for that organization? What does that look like in five years and what does it need to focus on to get there? 00;29;47;07 - 00;30;08;21 So I'll touch on three very important things for us. And I and I think, you know, that the ranking might be different depending on who you ask on our team, but global expansion is definitely a top priority for us. We want our RWD to power research all over the globe. We want to be a part of that movement and we want to facilitate that movement. 00;30;09;08 - 00;30;33;09 Extension of our data set is going to be very important and also with that extension of our platforms and our partnerships, we feel that there are many possibilities here to augment the current research and discovery processes with different types of data. We know that what makes up an individual and an individual's health, you know, only 20% of that is is health care data. 00;30;33;09 - 00;30;54;13 And what we do in hospitals and in practice, 80% of that is is more related to social determinants of health and our behaviors. So there is other data that we need to bring in as well to help that discovery in that personalized care and then leveraging the rural data to support other important initiatives is very important to us. 00;30;54;13 - 00;31;17;19 So rural data can help us leapfrog the current technical abilities that we have. I truly believe in AI and I know that our customers are dying to have that. So is that, you know, the easiest example I can give you that we need real data, real medical data to train AI and to create large language models that are more suited to health care. 00;31;18;03 - 00;31;24;05 And then, of course, we'll continue on our mission to to bring research into everyday practice. 00;31;24;20 - 00;31;45;24 With technology playing an ever increasing role in health care and how we deliver that health care to society. More of the focus does seem to be on landing on what role companies like Oracle can play. So I suppose my question is just that what's the appropriate role for a company like Oracle? What can it best do to shape the future of health care? 00;31;46;18 - 00;32;22;16 Well, I certainly don't want to simplify it. And, you know, I feel like we can we can do a lot here and and really make a big impact. But I feel in its most simplistic way that companies like ours are pivotal in enablement, in innovation. We have all the tools, advanced health care, we have experience to bring from other sectors and success in my mind is is not just being creative in building things that we think are cool tech, but, you know, really partnering and listening and understanding what clinicians and researchers need in solving for the right problems. 00;32;23;00 - 00;32;26;01 So that's how I see us as the conduit to get there. 00;32;26;17 - 00;32;34;18 Are there any really innovative products you're kind of seeing at Oracle that are especially relevant to the work you're doing and the goals that you're pursuing? 00;32;35;06 - 00;33;15;28 Well, I'm not going to lie. I am super excited about AI and how Oracle is applying AI to remove burden from health care. As a physician that suffered burnout in medical practice, this work is extremely important and it's also happening across life sciences, Oracle life sciences. So this is intelligence not only to decrease the huge amount of duplicative work that exists today, but also to be able to digest the overwhelming amount of data that we have in healthcare and provide more guided, guided decision support to clinicians and researchers and overall to improve safety for our patients. 00;33;16;12 - 00;33;54;15 I think that you had a chat with one of my colleagues who was working on the life sciences safety aspects of our work, and we are leveraging AI there to help read through tons of medical records to pick up those essential elements that are needed for Pharmacovigilance. I also wholeheartedly agree that employers, as often as they are today, should be a thing of the past and that health information needs to live in a different layer, needs to be more flexible, more usable for our patients, for our providers, and certainly for health delivery systems. 00;33;54;24 - 00;34;03;00 So Oracle is currently working on that and that's going to have a tremendous impact. And for our for us on the clinical research side as well. 00;34;03;08 - 00;34;17;15 Well, sounds exciting and we will, as they say, be watching that space very closely. Lu, thanks again for being with us. If someone wants to get in touch with you or learn more about your work or what Learning Health Network does. Is there a way they can do that? 00;34;18;02 - 00;34;44;27 Absolutely. You know, we welcome talking to any provider or organization that has EMR data to contribute. If you can contribute our data or health data in exchange for success, we want to talk to you. And this is regardless of whether you are an Oracle customer or not, today our RWD is EMR agnostic. We have data from at least 18 different health records and it's not exclusive. 00;34;44;29 - 00;34;55;07 So you can join multiple networks, but join ours as well. And you can reach out to us at Learning Health Network underscore at Oracle dot com. 00;34;55;16 - 00;35;27;13 Great Well if you are interested in how Oracle can simplify and accelerate your life sciences research, we invite you to check out Oracle dot com slash life dash sciences. Also be sure to subscribe to the show because there's more great insight and episodes ahead and join us next time on Research in Action.
Serious and unexpected adverse drug reactions – the ‘black swans' of pharmacovigilance – can place enormous strain on safety monitoring systems. Drawing examples from the COVID-19 pandemic, François Montastruc from Toulouse University Hospital explains how we can get better at dealing with the unpredictable.Tune in to find out:What Nassim Nicholas Taleb's ‘black swan' theory has to do with pharmacovigilanceWhat makes an adverse drug reaction a black, white, or grey swan Why flexibility and communication are key to patient safetyWant to know more?Here are the research articles cited in the episode:Quality of reporting of adverse events in clinical trials of COVID-19 drugs: systematic reviewPsychiatric disorders and hydroxychloroquine for COVID-19: a VigiBase studyHepatic disorders with the use of remdesivir for COVID-19Serious bradycardia and remdesivir for COVID-19: a new safety concernOxford-AstraZeneca COVID-19 vaccine-induced cerebral venous thrombosis and thrombocytopaenia: a missed opportunity for a rapid return of experienceAtypical thrombosis associated with VaxZevria® (AstraZeneca) vaccine: data from the French network of regional pharmacovigilance centresTeaching pharmacovigilance to French medical students during the COVID-19 pandemic: interest of distance learning clinical reasoning sessionsIf you enjoyed this podcast, check out these related episodes from the Drug Safety Matters archive:Reforming pharmacovigilance educationLessons in pandemic pharmacovigilanceIntuition in pharmacovigilanceJoin the conversation on social mediaFollow us on X, LinkedIn, or Facebook and share your thoughts about the show with the hashtag #DrugSafetyMatters.Got a story to share?We're always looking for new content and interesting people to interview. If you have a great idea for a show, get in touch!About UMCRead more about Uppsala Monitoring Centre and how we work to advance medicines safety.
What does a data hippie believe about the democratization of data? What role do technology companies, government, academia, industry, and other stakeholders play in life sciences and discovery? And how might walking clinical trials lead to improved precision medicine? We will get the answers to those questions and more in this episode with Dr. Chris Boone, the GVP of Research Services at Oracle Life Sciences. Chris has held some prominent roles at AbbVie and Pfizer, influencing health economics, medical epidemiology, and real-world data and evidence. He is an adjunct assistant professor at NYU, engaged in national health data committees, and serves on several boards including the American Heart Association.
The liver is the primary site for drug metabolism in the body, but it can be severely damaged by medicines or their toxic compounds. Rita Baião from the North Lisbon University Hospital Center reviews what pharmacovigilance professionals should know about drug-induced liver injury (DILI).Tune in to find out:Who is most at risk of developing DILIHow to diagnose the condition and control the damageHow to assess case reports of DILIWant to know more?This infographic in Nature Reviews nicely summarises the mechanisms, diagnosis, and management of drug-induced liver injury.In this report, the Council for International Organizations of Medical Sciences provides a global perspective on DILI detection, susceptibility factors, outcomes, and more.In this Drug Safety article, industry representatives outline how to identify, mitigate, and communicate the risk of DILI during drug development. The PRO-EURO DILI NETWORK coordinates research efforts on DILI across Europe and provides a forum to exchange knowledge and training on the topic. Similar initiatives include the Spanish DILI Registry and the Latin American DILI Network.The free online tool LiverTox contains up-to-date information on drug-induced liver injury for medicines and herbal products.To learn more about post-marketing surveillance and clinical care of DILI, check out Uppsala Monitoring Centre's free online course on the topic.For more on the clustering algorithm vigiGroup, revisit this interview with UMC scientists Jim Barrett and Joe Mitchell.Join the conversation on social mediaFollow us on X, LinkedIn, or Facebook and share your thoughts about the show with the hashtag #DrugSafetyMatters.Got a story to share?We're always looking for new content and interesting people to interview. If you have a great idea for a show, get in touch!About UMCRead more about Uppsala Monitoring Centre and how we work to advance medicines safety.
How, and to whom, should you disclose your MS at your job? And if you decide to disclose, is that information confidential? What does reasonable accommodation mean? If you need a day or two to bounce back after an infusion, is that part of your PTO? And what do you do when you no longer have days off available? If you're living with MS and you're employed, you absolutely need to know and understand your rights in the workplace. MS Navigator Christina Forster joins me to answer these questions and more as she shines a bright light on your rights in the workplace. I'll also remind you why I consider the MS Navigator program to be the gold standard when it comes to customized, one-on-one problem solving and support for anyone affected by MS. (And did I mention there's no charge???) I hear your questions, so we're taking a moment to review what the RealTalk MS show notes are all about, and where to find them. We're sharing study results that show a wheat-free diet may reduce MS severity (and it's not about the gluten!) We'll tell you about a call by experts to adopt a new framework for describing Parkinson's Disease (and we'll explain why that's relevant to the MS community) Scientists have identified 4 variant genes that place an individual taking some common MS disease-modifying therapies at higher risk of contracting PML. There's a test for that, and we'll tell you how to have that genetic testing done at no cost. We're talking about research that uncovered new details about how cells in the central nervous system communicate with the cells that produce myelin. And we'll tell you why zebrafish are the key to this research. And we're sharing a change in strategy for treating MS contained in the updated guidelines published by the Spanish Society of Neurology We have a lot to talk about! Are you ready for RealTalk MS??! This Week: Successfully navigating the health insurance maze :22 Why the MS Navigators are the gold standard when it comes to support for people affected by MS :56 Everything you need to know about the RealTalk MS show notes 2:12 STUDY: A wheat-free diet may reduce MS severity 5:28 Experts call for a new framework for describing Parkinson's Disease 8:31 A test to determine who is at high risk for contracting PML is available...at no cost 11:36 STUDY: Researchers identify specific proteins and signals involved in myelin formation in zebrafish 14:08 The Spanish Society of Neurology has updated its guidelines for treating MS 15:42 MS Navigator Christina Forster discusses your rights in the workplace 19:35 Share this episode 30:37 Have you downloaded the free RealTalk MS app? 30:57 SHARE THIS EPISODE OF REALTALK MS Just copy this link & paste it into your text or email: https://realtalkms.com/335 ADD YOUR VOICE TO THE CONVERSATION I've always thought about the RealTalk MS podcast as a conversation. And this is your opportunity to join the conversation by sharing your feedback, questions, and suggestions for topics that we can discuss in future podcast episodes. Please shoot me an email or call the RealTalk MS Listener Hotline and share your thoughts! Email: jon@realtalkms.com Phone: (310) 526-2283 And don't forget to join us in the RealTalk MS Facebook group! LINKS If your podcast app doesn't allow you to click on these links, you'll find them in the show notes in the RealTalk MS app or at www.RealTalkMS.com Contact an MS Navigator Phone: 1-800-344-4867 Web via chat: https://nationalmssociety.org Email: contactusnmss@nmss.org STUDY: Attenuation of Immune Activation in Patients with Multiple Sclerosis on a Wheat-Reduced Diet: A Pilot Crossover Study https://journals.sagepub.com/doi/10.1177/17562864231170928 Progressive Multifocal Leukoencephalopathy Genetic Risk Variants for Pharmacovigilance of Immunosuppressive Therapies https://www.researchgate.net/publication/371965313_OPEN_ACCESS_EDITED_BY_Progressive_multifocal_leukoencephalopathy_genetic_risk_variants_for_pharmacovigilance_of_immunosuppressive_therapies No Cost PML Risk Genetic Test https://pmlrisktest.org STUDY: Synaptic Input and Ca2+ Activity in Zebrafish Oligdodendrocyte Precursor Cells Contribute to Myelin Sheath Formation https://www.nature.com/articles/s41593-023-01553-8 Consensus Statement of the Spanish Society of Neurology on the Treatment of Multiple Sclerosis and Holistic Patient Management in 2023 https://www.sciencedirect.com/science/article/pii/S2173580824000191 Join the RealTalk MS Facebook Group https://facebook.com/groups/realtalkms Download the RealTalk MS App for iOS Devices https://itunes.apple.com/us/app/realtalk-ms/id1436917200 Download the RealTalk MS App for Android Deviceshttps://play.google.com/store/apps/details?id=tv.wizzard.android.realtalk Give RealTalk MS a rating and review http://www.realtalkms.com/review Follow RealTalk MS on Twitter, @RealTalkMS_jon, and subscribe to our newsletter at our website, RealTalkMS.com. RealTalk MS Episode 335 Guest: Christina Forster Privacy Policy
In this episode of "Queens of Quality," hosts Michelleanne, Jen, and guest Steve Thompson delve deeper into the ethical implications of AI in life sciences. They emphasize the necessity of an Algorithm Review Board (ARB) to oversee the ethical use of AI in clinical trials and beyond. Acknowledging the rapid evolution of technology and its complex application in healthcare, they stress the importance of collaboration and diverse representation to ensure ethical practices.Tune in!During this episode, you will learn about;00:31 Introduction and Welcome Back00:40 Discussion on AI and Clinical Trials01:17 The Concept of Algorithm Review Board01:44 The Evolution and Impact of Technology02:35 The Synthetic Patient Concept04:10 The Need for Ethical Standards in AI05:42 The Role of Institutional Review Boards08:49 The Importance of Diverse Representation10:48 The Idea of Algorithm Review Board10:59 The Need for Collaboration and Inclusion12:15 The Risks of Misusing AI in Medicine20:26 The Importance of Preventative Measures21:12 Call to Action for Audience Participation24:30 Conclusion and Invitation for Future Discussions Love the show? Subscribe, Rate, Review, Like, and Share!Let's Connect!Connect with Steve ThompsonLinkedIn: https://www.linkedin.com/in/stevethompsonsocal/Connect with Queens of Quality;Website: https://metisconsultingservices.com/LinkedIn: https://www.linkedin.com/company/metis-consulting-services/Email: hello@metisconsultingservices.com
International Data Corporation reports safety caseloads are increasing by 30% to 50% each year, and emerging technology will be the only way to keep up. But how are powerful technologies like generative AI advancing safety and pharmacovigilance? Is touchless case processing a good or bad thing? And how do we balance AI, automation, and the human touch? We will get answers to those questions and more in this episode with Bruce Palsulich, Vice President of Safety Solutions at Oracle Life Sciences. His portfolio includes Argus Safety, the industry-leading adverse event case processing and analytics solution, and Empirica Signal, the standard for signal detection and risk management. He has more than 30 years of experience in the healthcare and life sciences industry, including 25 in pharmacovigilance. -------------------------------------------------------- Episode Transcript: 00;00;00;00 - 00;00;13;22 What is pharmacovigilance? How can technology best handle the tracking of adverse drug events? And is touchless case processing a good or a bad idea? We'll get those answers and more on this episode of Research in Action. 00;00;15;01 - 00;00;18;28 The lead, the Building. 00;00;20;10 - 00;00;48;22 Hello, welcome to Research in Action, brought to you by Oracle Life Sciences. I'm Mike Stiles. Today we are talking with Bruce Palsulich, vice president of Safety Solutions at Oracle Life Sciences. Bruce's portfolio includes Argus Safety, the industry leading adverse event, case processing and analytics solution, and empirical signal, the standard for signal detection and risk management. He's got more than 30 years of experience in the healthcare and life sciences industry, including 25 and pharmacovigilance. 00;00;49;02 - 00;01;03;25 Now, why is that important? Well, International Data Corporation reports safety caseloads are increasing 30 to 50% each year. Bruce is intimately involved in tackling that volume. So, Bruce, thanks for thanks for being with us today. 00;01;04;05 - 00;01;06;00 Yeah, thanks, Mike. Happy to be here. 00;01;06;16 - 00;01;17;04 Yeah. Let's get acquainted with you first. How did Life's path bring you into life sciences technology? How did you kind of wind up at Oracle and what are you tasked with getting done there? 00;01;17;29 - 00;01;50;08 You know, back back when I was still in university, I actually started off doing software development and consulting with a medical device company. And so early in my career, it was working on the actual embedded software that controlled medical devices. And early on ended up joining a consulting firm that started off doing engineering, consulting on medical devices, and eventually working towards quality software and regulatory submissions. 00;01;50;24 - 00;02;17;04 And so came to Oracle in 2009. So we had acquired a company that was that small engineering startup that I mentioned. And this is the company that originally developed Argus Safety, so I managed the team that developed Argus safety originally and through my time at Oracle, I jumped out of a safety for a little while. 00;02;17;04 - 00;02;42;24 For about four years I was running our healthcare strategy. That was when we had a much smaller healthcare footprint than we now have with our acquisition of Cerner. But at the time we did a lot of things in sort of what was called health-information exchange, sort of the foundation for national platforms under Australia and Singapore and multiple provinces in Canada. 00;02;43;09 - 00;02;51;18 And after doing that for about four years and then I came back to the safety side of the business about ten years ago or so. 00;02;52;03 - 00;03;02;25 Well, did you always see yourself doing something in medicine and life sciences, like when you were younger, or did this was this a life path that kind of surprised you? 00;03;03;08 - 00;03;29;12 You know, I ommitted the part where for four years I actually worked in aerospace. So I even though when I was still at university, I started off in medical devices. I did take a job in aerospace for four years. But that's sort of left a hollow feeling and not the same sort of mission driven purpose. When you do have a role that's within the broader health care or clinical development. 00;03;29;12 - 00;03;55;04 So, you know, I think many people like myself that, you know, whether you're on the vendor side or whether you're on the the pharma side of drug safety or pharmacovigilance or even broader clinical development, I think you do appreciate that there's there's a calling and you feel more purpose driven life. I suppose working in a field that's helping individuals, helping patients. 00;03;55;26 - 00;04;13;27 Well, for our audience, and I'm deflecting because our audience is smart, this is mostly for me. Let's just level set. What's what's the main goal of safety and pharmacovigilance? And I imagine safety standards would apply across every step in that drug development process. 00;04;14;10 - 00;04;46;07 Yeah. So drug safety and pharmacovigilance is really trying to understand the safety of drugs that are under both clinical development as well as once they complete their clinical development and are approved for broad market use. And so clinical trials really focus on safety and efficacy, but that's done under controlled conditions with a limited number of patients and and sort of restricted patients as well. 00;04;46;07 - 00;05;27;27 And once a marketed drug is approved, it's going to be exposed to significantly more patients. And so during a clinical development, a clinical trial, if you had an adverse event that occurs in one out of 10,000 people, that's that's sort of defined as a rare adverse event or adverse reaction. You can imagine if you gave that to a billion people, maybe, for instance, in the example of the COVID vaccines, Now that rare adverse event that's only occurring in one out of 10,000 people is actually occurring 10,000 times in a billion people. 00;05;27;27 - 00;05;42;04 And so so really, you know, pharmacovigilance is looking at and trying to understand that benefit risk and manage that risk when it's being exposed under real world conditions to to actual patients. 00;05;42;24 - 00;06;11;20 So the study of a drug is hardly done after it's approved by the FDA and goes out into the public, the public market, that monitoring is still happening while safety is paramount, It can't be easy. I mean, for whatever reason, the public does seem to expect perfection without risk when it comes to their drugs. So, I mean, what are the biggest challenges that Pharmacovigilance and the industry has to deal with currently? 00;06;12;04 - 00;06;50;12 So, you know, getting back to sort of those controlled conditions that are under clinical trials, for instance, typically you're not looking at pediatric or children exposure. Quite often you're not dealing with elderly patients or immune compromised patients or patients taking multiple medications. You know, do you have the diversity within your clinical trials such that you're getting genetic differences that might exist within different populations and such? 00;06;50;12 - 00;07;21;16 And so so all of those are exposures that are going to occur during broad use of those products once they get approved. And so so pharmacovigilance is really trying to, you know, track that, trying to collect as many adverse reactions that occur. It's trying to evaluate whether or not those events truly are a reaction that's related to the drug that's being studied and the drug of interest. 00;07;21;16 - 00;07;46;15 Or is it just occurring, for instance, within the general background rate that you would expect within within a patient population? And so all of that analysis is to try and understand, is it more than correlation that just, you know, we have an adverse event that occurred with a drug? Is that coincidence or is that related to other drugs you're taking? 00;07;46;15 - 00;08;13;16 Is that a progression of the disease that the patient is taking a medication for, or is it something that is actually induced by by the drug of interest? And how serious is that reaction? And is that something that should be, you know, updated on the prescribing information that's tracked along with a drug and the, you know, communication and education that's done to the health care community. 00;08;13;16 - 00;08;16;08 So they understand the risks associated with the drug. 00;08;16;28 - 00;08;46;17 So I get the challenge, which is that in a clinical trial to get a drug approved and on the market, there's no way to cover every possible circumstance and every type of person and every type of situation where, like you said, there are other actions with other drugs. And I already get the enormity of the challenge of keeping track of all of those people, all of those interactions, all of those adverse effects. 00;08;46;20 - 00;08;59;13 I imagine technology is tackling those challenges, right, Or at least helping to tackle them. For instance, like how can we better efficiently do data management? How does that play a big role in tackling these problems? 00;08;59;28 - 00;09;24;29 Yeah, So the you know, we talked about the increasing volumes somewhat. It's still generally estimated that somewhere on the order of between five and 10% of the actual adverse events that occur are actually reported. And so many people might just say, well, I felt dizzy when I took that and so I stopped taking it. And, you know, did you ever tell your doctor, Well, no, I just manage that on my own. 00;09;24;29 - 00;09;56;21 So so really part of the challenge is how can you make it easier to collect a higher number of of these adverse reactions that actually occur? How can you reduce the burden on both the patient and on a health care professional to report those? The other is that, you know, we want to move beyond the handling and the workflow of processing these individual adverse event reports and get to a more of the emphasis being placed on driving or deriving insights from the data itself. 00;09;56;21 - 00;10;18;20 So so we want to make, as we deliver our own solutions, we want to make the pharma companies more efficient at being able to handle these sort of transactions. But with the real value out of that of then more, more effort and more value can be derived from the insights. From the data itself. 00;10;19;10 - 00;10;37;03 Yeah. I mean, there's a need to track adverse events that are happening all the time. The volume and the sources of that data increases exponentially. So you kind of touched on it there. How do you go about not just effectively managing the data flow but actually making it actionable? 00;10;37;14 - 00;11;18;12 So I think part part of this is, is within an ecosystem where perceptions are changing. And I'll say when I entered the field, you know, back in the mid nineties and such, the perception was sort of like an ostrich putting their head in the sand or something. And, and I don't want to know about what hasn't specifically been reported and, and Pharmacovigilance and drug safety was really looked at as sort of a a tax on the business a cost of doing business and wasn't appreciated as a valuable information asset that can be leveraged, you know, within a biopharma organization. 00;11;18;12 - 00;12;00;26 And so now I think PV data being an expensively curated data set, is now looked as a valuable information asset within organizations. It can be used to identify new indications, it can be used to inform drug discovery and portfolio prioritization. I think more and more we're seeing safety used as a competitive differentiator and certainly we saw that with the COVID vaccines and those that were commercially successful versus those that perhaps were perceived as having a more risks associated with those. 00;12;00;26 - 00;12;26;19 And towards this, I think, you know, we're looking at, you know, how can advances in data science, technology, things like machine learning, predictive models, generative AI, how can they be leveraged in order to process and be able to make use of these increasing volumes of information as well as diverse sources of adverse event information as well? 00;12;27;07 - 00;12;42;22 Yeah, that's where I want to go next. Are you seeing cloud based platforms and AI transforming pharmacovigilance? I mean kind of balance the hope and the hype for me. How do you see those technologies changing, how we approach drug safety and in like, say, the next decade or so? 00;12;43;05 - 00;13;18;23 So I really think and not not even just in this field, but in all fields, if you look at sort of the proliferation and the scaling of accumulation of data and information, it really requires new methods to approach that. So I do think that things like the large language models like Generative AI, are really going to be transformational into how we leverage this data and information specifically within health care and life science, but but also broader, I think, as a global population. 00;13;18;23 - 00;13;50;04 But so you can imagine even things like, you know, querying the data versus the natural language conversation, you know, perhaps you could ask how rare is this actual event or how does the rate of this adverse event compare for my drug versus other drugs within the same therapeutic class or given the volume of adverse events for this drug in 2023, how might how many reports might we expect to receive in in 2024? 00;13;50;04 - 00;14;26;08 Or are there clusters of patients that appear to be more likely to have this adverse event than other patients? And could you describe those differences? And so those I think, are all sort of examples that we're going to move from strictly having skills of of a data science list or query builder, a developer and such accessing data to sort of expose those questions of the data closer to the the individuals that are forming the question. 00;14;26;08 - 00;15;06;24 And so I think right now, you know, we really don't know what sort of insights or what sort of interactions are going to exist between these diverse data sources that are going to lead towards improved insights, improve patient safety. You know, we really want to, you know, identify what drugs work for, what patients and inversely know which patients shouldn't be exposed to certain drugs and and what characteristics, what scientific information is out there already, both broadly, you know, basic chemistry, genomics, pharmacokinetics, things like that. 00;15;07;12 - 00;15;10;29 But then bring that down to the experience of an individual patient. 00;15;11;18 - 00;15;24;29 Well, you've talked before about touchless case processing and what that could look like in the future. Tell us what that is and what companies should be doing now to start transitioning to that kind of model. 00;15;25;17 - 00;15;55;05 So I think sometimes the the phrase touchless case processing can sound a little scary, you know, that humans are going to be completely out of the loop and such. And I think the industry is generally looking for something a little bit more incremental. So we're not looking to say all cases should now be touchless. We're looking at things like, well, perhaps non-serious cases that don't provide a lot of new scientific information. 00;15;55;05 - 00;16;37;28 Perhaps those should be handled automatically by the system, perhaps for drugs that are well understood or have been on the market for a long time. Perhaps those would be better candidates for having automated case processing then things that are going to be a new a new drug on the market with less experience and exposure, perhaps cases that are received electronically and, you know, or cases from partners, you know, quite often they'll be global relationships between one pharma who partners with another pharma to to market that product in another region of the world. 00;16;37;28 - 00;17;03;22 And so you're receiving adverse event cases from this partner who who is originating those from patients or health care professionals. But if you're receiving that from a partner, you probably trust that they're sending it to you and maybe you can process that item automatically. The other is, is I think again, people get get a bit concerned if you say, well, this is going to be end to end and no human ever touched it. 00;17;03;22 - 00;17;35;06 And now we're going to be reporting this. You know, it doesn't necessarily have to be end to end. It can be the high volume of effort activities like doing the actual data entry. It can be decision support to support perhaps the causal assessments or to assess whether or not this is team serious or to look at is this an adverse event that's already listed on the the product label or prescribing information So it can be, you know, specific work steps are workflow steps. 00;17;35;06 - 00;18;15;27 Could be touchless, but overall, you know where it is appropriate. I think we still want humans in the loop to to oversee the process overall. So I think there are tremendous opportunities again, to take repetitive non value added processes out of and automate those from from requiring human effort to process those and allow the humans to focus on, you know, insights and focus on more value rather than these repetitive steps that that computers are well suited to be able to process as well. 00;18;15;27 - 00;18;39;08 You said something earlier, and that's very legitimate that, you know, a lot of patients will start taking a drug and experience some kind of adverse reaction to it and then just stop and not even tell their doctor about it. No one's ever going to know about the adverse reaction that they had. So there's even a reliability factor on the part of the patients and their willingness to report. 00;18;39;27 - 00;19;05;15 How far away are we from being able to have essentially a digital model of patients that drugs can be tested on? I mean, am I going way far ahead in the world of science fiction where in Silico gets kicked up a notch and safety procedures are tested on not real people, but essentially digital versions of patients? 00;19;05;15 - 00;19;35;22 Yeah, I think this whole concept and people may have heard the term digital twin and such is is obviously very interesting and I think we'll have certain benefit. I think, you know, certainly, you know, establishing toxicity and such would much better be supported through some of these models than than experimenting on on animals or on humans in order to establish toxicities and such. 00;19;35;22 - 00;20;12;08 And so so I think, you know, it's going to start from sort of the bottoms up that way when you're looking at those types of exposures. And I think as we get again, as we sort of stitch together these diverse data sources and have tools to be able to look for correlations and linkages that that are there, that would be difficult for humans to ascertain, then I think, you know, that will allow us to sort of advance these digital models that that represent a human response to medications and such. 00;20;12;08 - 00;20;44;18 So I think that's something that is definitely being advanced and we have pockets of that, and those pockets will ultimately end up being combined into a larger simulation of, you know, humans. So yeah, it's certainly an interesting area. And even myself, you know, it took me a while to sort of get my head around what that concept of digital twin and how that's going to benefit clinical development as well as is health care overall. 00;20;45;16 - 00;21;06;07 Well, we touched on the balance of hope and hype, but there's another balance here that you also touched on a bit. It feels like we want every advantage that technologies and automation and machines can bring us, but then we only trust those things up to a point. We do want human experience, human judgment and expertise to kind of have the final word. 00;21;06;07 - 00;21;13;22 So how do you view where that balance is now between tech and human? What gets us to the lowest error rates? 00;21;14;07 - 00;21;47;22 So I think, you know, one of the perception challenges that exists right now is that people think the humans are probably doing a better job than they really are right now. So if you gave the same health care record source document to five different people and said, you know, take from this piece of paper and enter it into the system, you would probably end up you would not end up with five identical versions of data entry from abstraction from that source medical record. 00;21;47;22 - 00;22;15;14 And so, you know, which one of those five is right. And what's the error rate there? And so I think you would normally say that humans are going to be somewhere on the order of six or 7% error rate for that type of work. And so even in manual processing is adverse event cases, typically there's going to be some sort of QC sampling that's trying to keep a handle on detecting errors and keep a handle on the overall process and such. 00;22;15;14 - 00;22;43;04 And so looking at how, you know, automation or machine learning is going to apply similar things are going to occur. You still want some checks and balances in order to know that you still have control of the automated process and things that are getting into medical judgment. I still think we we want to stick within what we would say is sort of augmented processing or decision support. 00;22;43;04 - 00;23;27;27 Speaker 3 So you want to provide assistance to the person making those judgments and say the system has determined that we think this might be related to this drug and based on these factors, why we think that might lead to that decision. Again, it would be up to the health care professional to make the final judgment there. So I think we are you're trying to bring the facts, bring the the right parameters and such into view so that the human can make the best decision, given the data points and the assessments that are being being suggested by by the system. 00;23;28;04 - 00;24;04;15 So I think we're still, you know, I was listening to NPR yesterday and they had a discussion on self-driving cars and there are self-driving cars ever going to get to the same accuracy and insights of of a human. And I think, you know, this is similar here, although probably, you know, certainly a different problem than looking at real time sensors in forming a automated self-driving car, but trying to look at human experience, human judgment, you know, how do we model some of those? 00;24;04;26 - 00;24;16;25 I think right now we'll stay in this augmented decision support mode for many of these, you know, clinical medical decisions and certainly leave the final judgment up to a clinician. 00;24;16;25 - 00;24;34;24 So, yeah, I remain terrified of human drivers. So in your role at Oracle Life Sciences, how is Oracle specifically leveraging these emerging technologies that we talked about like AI and big data to enhance drug safety and pharmacovigilance? 00;24;35;10 - 00;25;15;20 So there's a number of technologies and that's that's one of the benefits of being part of the broader Oracle, is that, you know, you kind of have all of these other areas and big areas of investment in AI and data science and high capacity compute and large language models and generative AI. And so so we get to it's like going to the toy store or something and decide which which things already have been built that you get to pull off the shelf and decide how we could apply those into our area of drug safety and pharmacovigilance. 00;25;15;20 - 00;25;45;13 And so, for instance, we just added the translation facility and, you know, out of the box in our Argus Cloud, you now have a translate button and it doesn't sound like a big deal, but if you were using an external tool before and then had to cut and paste and you were doing that 20 or 30 times within an adverse event report case to report it to local regions, just taking out that cut and paste and making it as a button straight in the system. 00;25;45;13 - 00;26;07;27 And by default we'll hook it up to the Oracle Cloud Translation Service. But if you wanted to hook it up to Google or you wanted to get up to a life science translation service, you could do that as well. Again, we're trying to look for where there are bottlenecks and we're trying to go out and look at where can we leverage an investment that Oracle's already making and then apply that into our specific field. 00;26;07;27 - 00;26;52;00 And, and part of that's what's exciting about our acquisition of Cerner is that, you know, I may have had a use case that sounded interesting in Pharmacovigilance. Maybe it's a case narrative generation or a case narrative is not all that different than a discharge summary for a health care record, or if you're doing a health care referral letter for referring the patient to a specialist and giving a summary of their their specific case and such, that's not that different than perhaps auto generating a letter that is a follow up request for collecting additional information on an adverse event case and so on. 00;26;52;00 - 00;27;17;28 Many of these there's there's overlap and we're able to team up with the teams that are focused on the health care use cases and add on our life science use cases and, you know, really benefit both teams or sometimes health care is leading the charge and sometimes life science is leading the charge. But ultimately that power together is like a multiplier, not not addition. 00;27;17;28 - 00;27;31;14 And and I think is a big benefit. And one of the big benefits of our of our acquisition of Cerner and the fact that we now are a leading health care company, in addition to, you know, what we've traditionally done in life science. 00;27;32;06 - 00;27;48;28 Yeah, there are a lot of industry players in life science. So is is what you describe what makes Oracle a real differentiator in the space when it comes to safety and pharmacovigilance? So things like combined assets and the Cerner acquisition. 00;27;49;11 - 00;28;25;29 Yeah I think there's there's a couple of things. One is sort of foundational with our cloud infrastructure and capacity there. For instance, we have high capacity compute and GPUs and just within our drug safety solution area, you know, we have two GP2 cloud instances available, dedicated 100% to our use and that's multimillion dollar worth of compute that we have dedicated to to our team of data scientists working to NPV. 00;28;25;29 - 00;28;58;05 And that would be difficult, not impossible, but difficult for a lot of other vendors to sort of dedicate that sort of compute capacity in such just to their life science use cases. Now the other I think is is around, you know, the acquisition of Cerner. So we talked about we now have a point of care footprint. So where, you know, clinicians are using Cerner software as the electronic health record when they're interacting with patients. 00;28;58;05 - 00;29;28;19 And so if we want to collect information as part of that point of care relationship, we can do that if we want to leverage, You know, we have something that's called the Learning Health Network that has a electronic health record, real world data asset. And so companies that our health systems sign on to use this because they they want a few benefits, they want access to clinical trials. 00;29;28;19 - 00;29;55;07 So they want their their patients and such to be able to be included within cohort selection and recruitment, site selection and recruitment for clinical trials. They also want to understand how they're delivery of care matches against other health systems across the country and eventually across the globe. So that they can sort of benchmark and compare how they're doing. 00;29;55;16 - 00;30;23;05 So that ends up creating this research data asset That, for instance, is very important for drug safety and pharmacovigilance, so that if you have a particular risk or an adverse event that's been reported against your drug or therapy, that you can then go out and say, well, is that just a correlation? Is there enough information within these individual cases to establish causality to the drug, actually cause that adverse reaction? 00;30;23;18 - 00;31;11;09 Or do I really need to go investigate that and understand its usage within the, you know, electronic health care record or claims data? And so so that's one of the areas that we are really focused on right now of sort of benefiting this better together with with the combined assets of and expertise between Oracle and Cerner is how can we leverage that real world data to understand and investigate risks that have been reported in adverse event reports to be able to go out and and understand real world usage there and and look at and understand how many patients are taking this drug, how many patients potentially had this reaction? 00;31;11;25 - 00;31;31;17 How many patients generally have this reaction not taking our drug, you know, understand those background rates and such. And so it's another level of understanding of the benefit risk once you have not only the adverse event reports, but the ability to research these within a real world dataset also. 00;31;32;03 - 00;31;38;03 Okay. I've got one more question for you. The all those warnings at the end of the pharma TV ads, is that because of you? 00;31;38;24 - 00;32;07;22 Well, ultimately, you know, I feel like sometimes we're plane name that tune or something. So a commercial comes on and I'll say, Oh, that's a pharma access to pharma y company. And you know, I'm usually right on naming the drug to that company. But, but it is, it is vitally important, you know, what is being done and where traditionally pharmacovigilance has sort of been a retrospective. 00;32;07;22 - 00;33;02;11 What can we learn after it has occurred? We're really trying to move towards what or labeling as precision pharmacovigilance, which is better understand that safety profile, better understand that risk benefit profile, not at these broad population levels that might be by by gender and age group, but getting down to smaller and smaller subpopulations and ultimately ideally to be able to go back and impact proactively the care of an individual patient where we might be able to identify based on a certain patient characteristics, a patient history, genomic marker, current labs, other concomitant medications they may be on presently, that maybe there is a higher risk to that individual patient of therapy versus therapy and provide that 00;33;02;11 - 00;33;39;18 information to the clinician that's treating the patient at that point of care. So so we intend to continue to drive towards that advances in drug safety that can improve overall population level help, but want to drive that down to to the ability to inform care around an individual patient. And thus, you know, when we see and hear those commercials and we hear the list of adverse events that are potentially associated with that drug, to give us better context, to say, well, what does that mean for me as bruise versus what does that mean for Mike? 00;33;39;18 - 00;33;51;15 And maybe one of us needs to be concerned and maybe one of us doesn't, and wouldn't that be great rather than just hear the list and and know that randomly that might be meaningful or not so obvious? 00;33;51;15 - 00;34;08;19 It's a vital part of drug development. And it's been interesting to hear what approaches are being taken and who's leading them. We appreciate you being on the show. For those who are interested in Pharmacovigilance and their interest has been tweaked, is there any way they can connect with you or get more information on what's going on? 00;34;09;09 - 00;34;51;14 So for me, I can be reached at Bruce.Palsulich@oracle.com. If you're on any one of your web search engines, you could just search on Oracle pharmacovigilance. The other is that we do have a community that we call the Oracle Safety Consortium. So if you search on Oracle Safety Consortium, you'll come up with and that's sort of our end user community where we have regular monthly events and such that are discussing industry, but as well as Oracle Solutions and how we're addressing the needs of industry through this sort of peer consortium group as well. 00;34;51;14 - 00;35;00;09 So those are sort of three ways that you could either follow up with me individually or learn more what we're doing here in Oracle for drug safety and Pharmacovigilance. 00;35;00;24 - 00;35;29;25 All right, we've got it. And if you want to see if Oracle can accelerate your life sciences research, just head over to Oracle dot com slash life dash sciences and you'll probably find out what you need to know. Don't forget to subscribe to this show and join us next time for Research in Action.
What did the drug safety community achieve in 2023 and how will the field develop in 2024? As the year comes to a close, we asked Angela Caro, president of the International Society of Pharmacovigilance (ISoP), to walk us through current and future trends in pharmacovigilance.Tune in to find out:Why patient engagement is a growing priorityWhat challenges exist in the Latin American regionWhich topics will be in the spotlight next yearWant to know more?ISoP is a non-profit society open to anyone with an interest in pharmacovigilance.Through 14 chapters and 13 special interest groups, the society works to enhance the safe and proper use of medicines across countries. Their latest annual meeting took place in Bali, Indonesia in November 2023, while the next one will take place in Montreal, Canada in October 2024. To learn more about ISoP's activities in patient engagement and pharmacogenomics, listen to these episodes from the Drug Safety Matters archive:Empowering patients as partnersTailoring drug therapy to your genesJoin the conversation on social mediaFollow us on Twitter, Facebook or LinkedIn and share your thoughts about the show with the hashtag #DrugSafetyMatters.Got a story to share?We're always looking for new content and interesting people to interview. If you have a great idea for a show, get in touch!About UMCRead more about Uppsala Monitoring Centre and how we work to advance medicines safety.
In this engaging episode of "Queens of Quality," Michelleanne welcomes Steve Thompson to delve into the realm of ethical AI in life sciences. They explore the excitement and challenges surrounding AI's evolution, emphasizing the critical need for an ethical compass in its application. With insightful discussions on bias, data integrity, and the multidisciplinary approach necessary for success, Steve and Michelleanne highlight the intricate balance between technological advancement and ethical responsibility. Their conversation teases a promising solution, promising a deeper exploration in the next episode.Tune in!During this episode, you will learn about;0:00:32 - Introduction and Guest Introduction (Steve Thompson) - Discussing the Importance of Ethical AI0:01:47 - Guest's Background and Journey into AI0:02:11 - The Current State and Future of AI0:03:22 - The Ethical Dilemmas in AI0:05:34 - The Importance of Continuous Improvement in AI0:11:35 - The Challenges of Creating Synthetic Patients0:13:32 - The Role of AI in Risk Management0:18:48 - The Importance of Multidisciplinary Teams in AI0:24:30 - The Ethical Considerations in AI0:28:48 - Conclusion and Teaser for Next Episode Love the show? Subscribe, Rate, Review, Like, and Share! Let's Connect! Connect with Steve ThompsonLinkedIn: https://www.linkedin.com/in/stevethompsonsocal/Connect with Queens of Quality;Website: https://metisconsultingservices.com/LinkedIn: https://www.linkedin.com/company/metis-consulting-services/Email: hello@metisconsultingservices.com
To mark #MedSafetyWeek, which took place from 6–12 November, we're releasing a special two-part episode on pharmacovigilance communication campaigns. In this second part, we hear from three #MedSafetyWeek veterans – Anne-Cécile Vuillemin from the Ministry of Health in Luxembourg, Ban Al-Shimran from the Iraqi Ministry of Health, and Frieda Shigwedha from the Therapeutic Information and Pharmacovigilance Centre in Namibia – about what makes a successful campaign.Tune in to find out:Why you should always tailor your communication strategy to your settingHow to deal with the financial, cultural, and logistical challenges of campaign planningWhat to keep in mind if you are new to #MedSafetyWeekWant to know more?You can read a summary of this episode on the Uppsala Reports news site.To learn more about #MedSafetyWeek, check out the hashtag online and visit the campaign website, where you will also find free social media materials in several languages.This is the second of a two-part episode on pharmacovigilance communication campaigns. Listen to the first part here.Join the conversation on social mediaFollow us on Twitter, Facebook or LinkedIn and share your thoughts about the show with the hashtag #DrugSafetyMatters.Got a story to share?We're always looking for new content and interesting people to interview. If you have a great idea for a show, get in touch!About UMCRead more about Uppsala Monitoring Centre and how we work to advance medicines safety.
To mark #MedSafetyWeek, which takes place from 6–12 November, we're releasing a special two-part episode on pharmacovigilance communication campaigns. In this first part, we speak to Mitul Jadeja from the Medicines and Healthcare products Regulatory Agency in the UK about under-reporting and how initiatives like #MedSafetyWeek can help draw attention to medicines safety.Tune in to find out:Why under-reporting plagues all pharmacovigilance systemsWhat regulators can do to encourage people to report side effectsWhy we need reports from both patients and healthcare professionalsWant to know more? Here are the studies cited in the episode:A BMJ study in 2022 measured the burden and associated cost of adverse drug reactions, polypharmacy and multimorbidity at a hospital in the UK.In 1976, Inman proposed a theoretical model, known as the ‘seven deadly sins', to explain why healthcare professionals fail to report adverse drug reactions. This recent systematic review in Drug Safety expands on that. The SCOPE Joint Action project aimed to enhance pharmacovigilance in the EU and delivered practical guidance for regulators.The first UK study to compare Yellow Card reports from patients and healthcare professionals was published in 2012.To join the #MedSafetyWeek campaign next week, follow the hashtag online and check out the campaign website for free social media materials.Finally, don't forget to tune in on 13 November for part 2 of this podcast, where we'll hear from #MedSafetyWeek advocates in Iraq, Luxembourg, and Namibia about their experience with the campaign. Read a preview of the conversation on Uppsala Reports.Join the conversation on social mediaFollow us on Twitter, Facebook or LinkedIn and share your thoughts about the show with the hashtag #DrugSafetyMatters.Got a story to share?We're always looking for new content and interesting people to interview. If you have a great idea for a show, get in touch!About UMCRead more about Uppsala Monitoring Centre and how we work to advance medicines safety.
The FDA Group's CEO, Nick Capman, sits down with Steve Knowles, MB.BS, M.R.C.P., M.F.P.M., Chief Medical Officer at Halozyme, a biopharmaceutical company bringing disruptive solutions to significantly improve patient experiences and outcomes for emerging and established therapies. They discuss advancements in drug delivery technology and the role of leadership in fostering innovation and addressing challenges in this field. Discussion points include: » Dr. Knowles's transition from a physician in the UK to Chief Medical Officer at Halozyme in San Diego. » The evolution in drug delivery systems, such as the shift from hospital-based treatments to self-administration of drugs at home and the introduction of auto-injectors and on-body devices for efficient, patient-friendly drug administration. » Addressing human factors and compliance to ensure products are user-friendly—and the importance of human factor studies in product development. » The advantages of subcutaneous drug delivery, including reduced side effects, improved pharmacokinetic profiles, and patient convenience. » How Halozyme's one-team approach fosters a culture of curiosity, flexibility, and feedback. » Aligning individual and organizational goals to drive innovation. Dr. Knowles has deep pharmacovigilance and medical affairs experience gained over a career spanning almost 20 years. Dr. Knowles joined Halozyme in January 2018 as Vice President, Drug Safety & Pharmacovigilance. He is responsible for the Medical, Regulatory and Drug Safety organizations. Prior to Halozyme, Dr. Knowles served as Senior Medical Director, Global Patient Safety and Benefit Risk Management at Eli Lilly & Co. where he led the global safety physician/scientist group responsible for overseeing the safety profiles and benefit risk management of medicines across all therapeutic areas and phases of development and supported numerous BLA and MAA submissions. During his 16 years at Lilly, he held positions in Medical Affairs and from 2005 to 2017 he held roles of increasing responsibility within Global Patient Safety. Prior to these roles, Dr. Knowles spent more than 17 years in clinical practice in the UK in both hospital-based and general practice roles. Dr. Knowles received his Bachelor of Medicine and Surgery degrees (MB.BS) from the University of Newcastle Upon Tyne and is a Member of the Royal College of Physicians (MRCP) and a Member of the Faculty of Pharmaceutical Medicine (MFPM). Who is The FDA Group? The FDA Group helps life science organizations rapidly access the industry's best consultants, contractors, and candidates. Our resources assist in every stage of the product lifecycle, from clinical development to commercialization, with a focus in Quality Assurance, Regulatory Affairs, and Clinical Operations. https://www.thefdagroup.com/
Herbal remedies have been used for thousands of years to treat what ails us. Yet why do we still know so little about their potential side effects compared to modern medicines?This episode is part of the Uppsala Reports Long Reads series – the most topical stories from UMC's pharmacovigilance news site, brought to you in audio format. Find the original article here.After the read, we speak to author Daniele Sartori to learn more about the challenges in herbal pharmacovigilance.Tune in to find out:Why the risks of herbals are rarely discussedHow to encourage safety data collection for herbalsHow to improve herbal nomenclature and regulationWant to know more?Here are some of the resources cited in the episode:Underreporting of adverse reactions to herbal remedies is driven by our attitude towards herbals themselves, but also by a lack of training on herbal medicines in healthcare curricula.It is possible to safely use herbal medicines together with other medicines, but we must keep in mind some critical issues related to their interaction.Simple videos can dramatically increase public awareness of ADR reporting schemes.Kew Gardens' Medicinal Plant Names Services offers a systematic overview of medicinal plants and their accepted scientific names.The American Botanical Council suggests methods to uncover attempts to adulterate plant extracts.For a comprehensive overview of herbal pharmacovigilance, check out this recent book by Joanne Barnes and colleagues covering advances, challenges, and international perspectives in the field.For more on Daniele's scoping review of signals or the thorny nomenclature of medicinal plants, listen to these episodes from the Drug Safety Matters archive: The evidence for signalsNavigating the plant names jungleFinally, don't forget to subscribe to the monthly Uppsala Reports newsletter for free regular updates from the world of pharmacovigilance.Join the conversation on social mediaFollow us on Twitter, Facebook or LinkedIn and share your thoughts about the show with the hashtag #DrugSafetyMatters.Got a story to share?We're always looking for new content and interesting people to interview. If you have a great idea for a show, get in touch!About UMCRead more about Uppsala Monitoring Centre and how we work to advance medicines safety.
A patient's perspective can ensure safe medical care and lead to new insights that traditional research may overlook. But how do we best harness that perspective to improve patient outcomes?This episode is part of the Uppsala Reports Long Reads series – the most topical stories from UMC's pharmacovigilance news site, brought to you in audio format. Find the original article here.After the read, we speak to Manal Younus, who authored the article, to learn more about patient engagement and its benefits for medicines safety.Tune in to find out:How regulators and healthcare professionals can effectively engage patientsHow patients can get involved in drug safety monitoringWhat the pharmacovigilance community learned from the valproate caseWant to know more?In 2022, the Council for International Organizations of Medical Sciences (CIOMS) published a comprehensive report on patient involvement in the development, regulation and safe use of medicines. They also recorded a webinar to summarise the report's main conclusions.The International Society of Pharmacovigilance (ISoP) runs a patient engagement group to advance patient involvement in the safety monitoring of medicines.The Valproate toolkit, developed by the UK's Medicines and Healthcare Products Regulatory Agency (MHRA), supports healthcare professionals in advising women of childbearing age about the risks and benefits of valproate therapy.PatientsLikeMe is a digital platform where patients can share personal health stories, connect to peers, and learn about different conditions and treatments.For more on patient engagement and communication, check out these episodes from the Drug Safety Matters archive:Why we should listen to patientsThe challenge of rare diseasesHow to talk about risksFinally, don't forget to subscribe to the monthly Uppsala Reports newsletter for free regular updates from the world of pharmacovigilance.Join the conversation on social mediaFollow us on Twitter, Facebook or LinkedIn and share your thoughts about the show with the hashtag #DrugSafetyMatters.Got a story to share?We're always looking for new content and interesting people to interview. If you have a great idea for a show, get in touch!About UMCRead more about Uppsala Monitoring Centre and how we work to advance medicines safety.
Medicines safety monitoring is a continuous process that begins with pre-marketing clinical trials and continues with post-marketing studies to fill any gaps in knowledge. With Marianne Lunzer from AGES and Sanja Prpić from HALMED, we review the pros and cons of various study types and the importance of testing medicines on diverse populations.Tune in to find out:How pre- and post-approval safety studies are connectedWhy safety assessors can request studies in underrepresented populationsHow new regulations are impacting safety assessments in the EUWant to know more?This review in Trials summarises the methodological challenges of assessing drug safety in clinical trials, while this study in Clinical and Translational Science reviews how sex, racial, and ethnic diversity in clinical trials have changed in recent years. Post-authorisation safety studies can be imposed or voluntary and can be carried out as clinical trials or as non-interventional studies. Read about the differences on the European Medicines Agency's website.Large simple trials can control for biases in observational research while still providing results that are generalisable to real-world use. This review in Drug Safety explains why.The new Clinical Trials Regulation harmonises how EU trials are assessed and supervised for increased safety and transparency. As part of these efforts, the SAFE CT project aims to facilitate clinical trial coordination and safety assessments in the EU.For more on clinical trials, revisit this conversation with Peter Doshi on restoring invisible and abandoned trials.This episode is the last of a three-part series on sources of evidence in pharmacovigilance. Listen to the first two episodes here:The evidence for signalsUnlocking the power of real-world dataJoin the conversation on social mediaFollow us on Twitter, Facebook or LinkedIn and share your thoughts about the show with the hashtag #DrugSafetyMatters.Got a story to share?We're always looking for new content and interesting people to interview. If you have a great idea for a show, get in touch!About UMCRead more about Uppsala Monitoring Centre and how we work to advance medicines safety.
Healthcare professionals are key players in medicines safety but they often lack the time or knowledge to report adverse drug reactions. To change that, we need to urgently rethink how we teach pharmacovigilance, argues Michael Reumerman from Amsterdam University Medical Centers.Tune in to find out:How real-life education can benefit healthcare studentsWhich educational intervention can be most impactfulHow adverse drug event managers can improve pharmacovigilanceWant to know more?In his PhD thesis, Michael details the current state of pharmacovigilance education and all the real-life interventions he and his colleagues have tested in the Netherlands so far.As part of an international collaboration, staff at Amsterdam UMC have helped set up the European Open Platform for Prescribing Education (EurOP2E), an online collection of problem-based, open teaching resources to improve clinical pharmacology and therapeutics education.The World Health Organization's Guide to Good Prescribing provides a six-step guide for students to the process of rational prescribing – but the time has come to update both its content and form.In 2018, the Netherlands Pharmacovigilance Centre Lareb developed a core curriculum for pharmacovigilance education in universities.Whether you're a healthcare professional or not, check out Uppsala Monitoring Centre's growing collection of self-paced e-learning courses to learn about different aspects of pharmacovigilance.Join the conversation on social mediaFollow us on Twitter, Facebook or LinkedIn and share your thoughts about the show with the hashtag #DrugSafetyMatters.Got a story to share?We're always looking for new content and interesting people to interview. If you have a great idea for a show, get in touch!About UMCRead more about Uppsala Monitoring Centre and how we work to advance medicines safety.
Caris Precision Oncology Alliance™ Chairman, Dr. Chadi Nabhan, sits down with Dr. R. Donald Harvey, Professor Department of Hematology and Medical Oncology at Emory University School of Medicine and Director of Winship Cancer Institute's Phase I Clinical Trials Unit. Together they discuss the role of the pharmacist in pharmacovigilance and pharmacoadherence, and how we can ensure that patients are being properly educated and compliant with the new products being approved each day. For more information, please visit: www.CarisLifeSciences.com
The vast amount of real-world data collected during routine clinical care is a treasure trove of safety information – but there are challenges to overcome before this rich source of evidence can be applied to pharmacovigilance. Patrick Ryan from Johnson & Johnson discusses how to harness real-world data for patient safety.Tune in to find out:How real-world data is collected and analysedWhich pharmacovigilance processes will benefit most from itHow to make data accessible without infringing patient privacy Want to know more?Review the basics of real-world data and its use in the medicines life cycle in Pharmaceutical Medicine, or read up on the opportunities and challenges for pharmacovigilance in Clinical Pharmacology & Therapeutics.In partnership with the Observational Health Data Sciences and Informatics (OHDSI) and the European Health Data & Evidence Network (EHDEN) consortia, UMC researchers are exploring how real-world data can help prioritise and validate signals in pharmacovigilance. Read about their latest collaboration on Uppsala Reports.Another important player in the real-world data space is the Data Analysis and Real World Interrogation Network (DARWIN), which aims to provide timely and reliable evidence from real-world healthcare databases in the EU to improve the safety and effectiveness of medicines.For more on real-world evidence and the challenges of working with big data, don't miss the Voice of EHDEN podcast or this conversation with Elena Rocca from the Drug Safety Matters archive.This episode is the second of a three-part series on sources of evidence in pharmacovigilance. Listen to the other two episodes here:The evidence for signalsAssessing safety in clinical trialsJoin the conversation on social mediaFollow us on Twitter, Facebook or LinkedIn and share your thoughts about the show with the hashtag #DrugSafetyMatters.Got a story to share?We're always looking for new content and interesting people to interview. If you have a great idea for a show, get in touch!About UMCRead more about Uppsala Monitoring Centre and how we work to advance medicines safety.
Spontaneous reports of adverse drug reactions are a common source of evidence in pharmacovigilance, but as the science evolves, so do the types of data used to find and assess signals. Uppsala Monitoring Centre's Daniele Sartori reviews how signal detection practices have changed over time.Tune in to find out: Which features of case reports are most often used to assess causality Why pharmacovigilance experts should report clinical assessments clearly How to shorten the time between signal detection and communication Want to know more? Check out the full scoping review that inspired this episode.In 2002, Meyboom and colleagues discussed criteria to select and follow up on signals.In the first chapter of Uncertainty in Pharmacology, Aronson explains the difference between evidence for a mechanism and evidence from a mechanism.In 2018, Murad and colleagues published a method to evaluate the quality of evidence in a series of case reports.UMC scientists have shown how chemical information can support timely signal detection.This episode is the first of a three-part series on sources of evidence in pharmacovigilance. Listen to the other two episodes here:Unlocking the power of real-world dataAssessing safety in clinical trialsJoin the conversation on social mediaFollow us on Twitter, Facebook or LinkedIn and share your thoughts about the show with the hashtag #DrugSafetyMatters.Got a story to share?We're always looking for new content and interesting people to interview. If you have a great idea for a show, get in touch!About UMCRead more about Uppsala Monitoring Centre and how we work to advance medicines safety.
Commentary by Dr. Ilana Schlam
“The way I approach patient education conversations is to discuss it, address it, but not to emphasize it. I really like to focus on what the drug is. I mention if it's a biosimilar, I explain it, I give resources if they want it, but I really try to focus on the things that they're going to need to know in order to help be part of their care, but have readily available information to give them if they want more,” ONS affiliate member Rowena (Moe) Schwartz, PharmD, BCOP, professor of pharmacy practice at the University of Cincinnati in Ohio, told Jaime Weimer, MSN, RN, AGCNS-BC, AOCNS®, oncology clinical specialist at ONS during a discussion about the basics of biosimilars for nurses and patients. You can earn free NCPD contact hours after listening to this episode and completing the evaluation linked below. Music Credit: “Fireflies and Stardust” by Kevin MacLeod Licensed under Creative Commons by Attribution 3.0 Earn 0.5 contact hours of nursing continuing professional development (NCPD) by listening to the full recording and completing an evaluation at myoutcomes.ons.org by February 3, 2025. The planners and faculty for this episode have no relevant financial relationships with ineligible companies to disclose. ONS is accredited as a provider of NCPD by the American Nurses Credentialing Center's Commission on Accreditation. Learning outcome: The learner will report a gain in knowledge related to biosimilars. Episode Notes Complete this evaluation for free NCPD. ONS Biosimilars Learning Library ONS Voice articles: FDA Publishes Three New Biosimilars Resources for Healthcare Providers Nurses Will Forge New Territory With Biosimilars in Cancer Care Biosimilars, Oral Agents, and Drugs Targeted Genetic Mutations Are Creating a Paradigm Shift in Cancer Treatment Clinical Journal of Oncology Nursing articles: Approval Process: An Overview of Biosimilars in the Oncology Setting Nursing Roles: Clinical Implications Regarding Trends, Administration, and Education for Biosimilars in Oncology Practice Biosimilars: Exploring the History, Science, and Progress Clinical Trials: Nursing Roles During the Approval Process and Pharmacovigilance of Biosimilars American Society of Clinical Oncology Statement: Biosimilars in Oncology American Journal of Health-System Pharmacy article: Biosimilar Strategic Implementation at a Large Health System S. Food and Drug Administration (FDA) resources on biosimilars Nonproprietary Naming of Biological Products Purple Book Database of Licensed Biological Products Patient education Review and approval Hematology/Oncology Pharmacy Association International Society of Oncology Pharmacy Practitioners To discuss the information in this episode with other oncology nurses, visit the ONS Communities. To provide feedback or otherwise reach ONS about the podcast, email pubONSVoice@ons.org. Highlights From Today's Episode “For generic products, it's important that they are the same as the brand-name product. The differences there tend to be only inactive ingredients. And for a biosimilar, it's very similar to that biologic, and it's supposed to have no clinically meaningful differences from the reference product.” Timestamp (TS) 06:46 “There was a lot of discussion about how would we identify when a patient got a biosimilar? A naming convention was implemented in 2017 that would help address understanding what particular drug a patient got at every point in care. The naming is done so that you have the core nonproprietary name, and then there's a four letter suffix added. . . . That naming convention was for all biologics that were approved that U.S. Food and Drug Administration naming guidance was implemented. And it's so that when that biologic comes out, if a biosimilar is ever approved, you would be able to differentiate.” TS 12:50 “The way I approach patient education conversations is to discuss it and address it but not to emphasize it. Because then I think it almost creates a question in the person's mind, ‘Is this as good?' We saw that with generics, we see that with biosimilars, and I really think that people need to know that this is the drug that you're using. They're pretty much overwhelmed, just even about the side effects. So I really like to focus on this is what the drug is, I mention if it's a biosimilar, I explain it, I give resources if they want it, but I really try to focus on the things that they're going to need to know in order to help be part of their care but have readily available information to give them if they want more.” TS 17:19 “As we get more of these products, as we use them, I think that the healthcare team is becoming more comfortable. And I think that is definitely felt by patients, caregivers, and families. As people get more comfortable with the data and the understanding of these, I think that will help patients and kind of flow over to the whole team.” TS 19:06
Most pharmacovigilance professionals will have heard of masking – a statistical issue where reports for one drug hide signals for other drugs. But the problem gained fresh attention when record amounts of reports began piling up for the COVID-19 vaccines. How should we be unmasking data in the COVID-19 vaccine era?This episode is part of the Uppsala Reports Long Reads series – the most topical stories from UMC's pharmacovigilance magazine, brought to you in audio format. Find the original article here. After the read, we speak to data scientist Sara Vidlin, who authored the article, to learn more about masking and how to deal with it.Tune in to find out: How masking evolves with the data Which methods can be used to unmask data What other pitfalls to watch out for when performing quantitative analysesWant to know more?In the very beginning of the vaccine rollout, the USA FDA observed how early signals for COVID-19 vaccines were delayed because of other drugs masking them, highlighting how masking is not a static phenomenon.In 2013, Uppsala Monitoring Centre developed a simple strategy to uncover masking by identifying and removing influential outliers.Join the conversation on social mediaFollow us on Twitter, Facebook or LinkedIn and share your thoughts about the show with the hashtag #DrugSafetyMatters.Got a story to share?We're always looking for new content and interesting people to interview. If you have a great idea for a show, get in touch!About UMCRead more about Uppsala Monitoring Centre and how we work to advance medicines safety.
In this episode, Marta Boffito, MD, PhD, FRCP, and Jens D. Lundgren, MD, DMSc, address key considerations when evaluating antiretroviral therapy safety and tolerability in aging patients and those with possible cardiometabolic toxicities, including:Monitoring for cardiometabolic syndromes (eg, lipid panels, coronary artery calcification scores)Approaching antiretroviral-related weight gain in clinical practiceInterpreting results from RESPOND on cardiovascular risk with integrase strand transfer inhibitorsCollaborating with other specialties (eg, cardiologists, dietitians) to provide a multidisciplinary approach for managing comorbidities, including prevention and managementFaculty: Marta Boffito, MD, PhD, FRCPConsultant Physician/ProfessorHIV/ResearchChelsea and Westminster HospitalImperial College LondonLondon, United KingdomJens D. Lundgren, MD, DMScProfessorRigshospital, University of Copenhagen DirectorCentre of Excellence for Health, Immunity and Infection (CHIP)Rigshospital, University of CopenhagenCopenhagen, DenmarkLink to full program:http://bit.ly/3PM3nYeLink to downloadable slides: http://bit.ly/3WgYycz
This GOLD Nugget episode sees Helena reflect on some of the best insights offered by Pav Rishiraj, Director and Head of Pharmacovigilance at Ipsen. She shares some of Pav's thoughts on how the often under-recognised field of pharmacovigilance has changed – particularly its rapid evolution during the COVID-19 pandemic – and its importance as a strategic enabler, as well as Pav's advice on what pharma should do to ensure teams feel valued and integral to the businesses they serve. If you're interested in learning more about the topic areas discussed in this episode, check out the following content: Pav's full episode: Pharmacovigilance past, present and future https://www.emg-gold.com/post/podcast-pharmacovigilance-past-present-and-future The physics of pharmacovigilance https://www.emg-gold.com/post/the-physics-of-pharmacovigilance
“It's incumbent on us as nurses to stay informed about these newly approved drugs or new indications in drugs because we're the front line in helping these patients manage adverse events,” Teresa Knoop, MSN, RN, AOCN®, nurse consultant in Nashville, TN, told Jaime Weimer, MSN, RN, AGCNS-BC, AOCNS®, oncology clinical specialist at ONS, during a conversation about the latest updates and approvals in oncology pharmacology. You can earn free NCPD contact hours after listening to this episode and completing the evaluation linked below. Music Credit: “Fireflies and Stardust” by Kevin MacLeod Licensed under Creative Commons by Attribution 3.0 Earn 1 contact hour of nursing continuing professional development (NCPD) by listening to the full recording and completing an evaluation at myoutcomes.ons.org by January 13, 2025. The planners and faculty for this episode have no relevant financial relationships with ineligible companies to disclose. ONS is accredited as a provider of NCPD by the American Nurses Credentialing Center's Commission on Accreditation. Learning outcome: Participants will report an increase in knowledge related to the latest updates and approvals in oncology pharmacology. Episode Notes Complete this evaluation for free NCPD. Oncology Nursing Podcast Episode 126: Oncology Clinical Trials and Drug Development ONS Voice articles: S. Food and Drug Administration (FDA) updates Drug reference sheets Predictive and Diagnostic Biomarkers: Identifying Variants Helps Providers Tailor Cancer Surveillance Plans and Treatment Selection Help Patients Understand Biomarker Test Results and Clinical Trials Options Use ClinicalTrials.gov to Find the Right Cancer Research Studies for Your Patients ONS Biomarker Database ONS Genomics and Precision Oncology Learning Library ONS Immuno-Oncology Learning Library ONS Oral Anticancer Medication Learning Library ONS Biosimilars Learning Library ONS Seal of Approval Library Oral Chemotherapy Education Sheets Intravenous Cancer Treatment Education Sheets Clinical Journal of Oncology Nursing article: Clinical Trials: Nursing Roles During the Approval Process and Pharmacovigilance of Biosimilars FDA resources: Drug development and approval process Oncology/hematologic malignancies approval notifications Ongoing cancer accelerated approvals Verified clinical benefit cancer accelerated approvals Withdrawn cancer accelerated approvals Project Renewal Biosimilars review and approval Drug Information Soundcast in Clinical Oncology (D.I.S.C.O.) Project Livin' Label Oncology Center of Excellence To discuss the information in this episode with other oncology nurses, visit the ONS Communities. To provide feedback or otherwise reach ONS about the podcast, email pubONSVoice@ons.org. Highlights From Today's Episode “Full approval through the Center for Drug Evaluation and Research (CDER) and FDA means that those drugs have gone through the laboratory testing, human clinical trial testing, and very extensive clinical trials to make sure that they are effective and that the benefits of those drugs outweigh the risks.” TS 02:28 “In 1992, CDER established a new program that would help these drugs get expedited, particularly in life-threatening or serious diseases like cancer. So they established an accelerated approval pathway for these promising therapies. They were hoping to shorten that period of time, and a number of our cancer-fighting drugs have come onto the market through this accelerated approval pathway.” TS 04:29 “When a drug gets an FDA approval, whether it be accelerated or final, then typically they get approved for one, possibly two indications on that first approval. But there are clinical trials ongoing in other diseases and in other indications. So we will then see drugs—after those clinical trials are conducted—taken to CDER for approval for that new indication.” TS 24:02 “The exciting thing for our patients is that new indications often treat more advanced cancers in which you discover a biomarker and could give patients potential treatment options when other options may have been exhausted.” TS 27:43 “It's incumbent on us as nurses to stay informed about these newly approved drugs or new indications in drugs because we're the front line in helping these patients manage adverse events. Many of these drugs are designed so patients have to stay on them for extended periods of time, or at least they get the greatest benefit by staying on it for extended periods of time. Our patient education is key in helping patients do that.” TS 34:50 “In 2023, I think we will continue to see many new drugs that are approved. We will see new indications. I think particularly we will continue to see cellular therapy agents developed—we'll see them gain new indications. I would be willing to forecast that we're going to see more and more of specific immunomodulatory drugs or those antibody drug conjugates—all of those drugs that are designed to treat the cancer in a couple of different ways.” TS 37:29
On this episode, I was joined again by Dr. Khaudeja Bano, Vice President of Combination Product Quality at Amgen. On this episode Khaudeja and I discuss: - PMSR and Risk Management - Patient Safety, Pharmacovigilance, Medical Affairs, Clinical Affairs in Medical Device and Pharma companies. - PMSR Regulatory Landscape Globally (Outside of the US) Dr. Khaudeja Bano is the VP of Combination Products Quality at Amgen. Executive director of combination product safety. She's held multiple roles at other companies as well like Abbvie, Abbott, and Guidant and is the chair of the Post Market Safety committee for the combination products coalition.
Pharmacovigilance is the process of monitoring the effects of medical drugs after they have been licensed for use, especially to identify and evaluate previously unreported adverse reactions.Welcome to another incredible episode with your hosts, Jennifer and Michelleanne. In this episode, we focus on pharmacovigilance in the pharmaceutical industry. We talk about who is responsible for drug safety & pharmacovigilance, the sources of pharmacovigilance data, and pharmacovigilance in clinical trials. In addition, we discuss the Investigator's Brochure (IB) and Adverse Event (AE) reporting policy and share how you can turn pharmacovigilance into a competitive advantage. Tune in! During this episode, you will learn about;[00:01] Episode introduction[00:35] Today's focus; pharmacovigilance [01:23] Definition of pharmacovigilance[04:20] Who is responsible for drug safety & pharmacovigilance?[05:54] Sources of pharmacovigilance data[08:40] Pharmacovigilance in clinical trials [10:00] Adverse Event (AE) reporting policy[14:33] Investigator's Brochure (IB)[16:30] Postmarketing surveillance and adverse drug reactions[20:03] A recap of the episode[22:21] Turning pharmacovigilance into a competitive advantage[26:04] How to connect with us Love the show? Follow, Rate, Review, Like, and Share! Let's Connect!Website: https://metisconsultingservices.com/LinkedIn: https://www.linkedin.com/company/metis-consulting-services/Email: info@metisconsultingservices.com
Joining Chadi on today's show is Donald Harvey, PharmD, BCOP, FCCP, FHOPA, Winship Cancer Institute of Emory University. Dr. Harvey shares available career paths and how the pharmacist role has evolved over the years, how pharmacists continue to stay involved with patients after discharge, strategies he uses to help cancer patients adhere to oral oncolytics, and his “Call to Action” to improve such adherence – as co-planned by the FDA and ASCO and published in Journal of Clinical Oncology. The discussion is very detailed and enlightening; you won't want to miss it! View Dr. Harvey's publication “Call to Action for Improving Oral Anticancer Agent Adherence.” https://ascopubs.org/doi/10.1200/JCO.21.02529 Check out Chadi's website for all Healthcare Unfiltered episodes and other content. www.chadinabhan.com/ Watch all Healthcare Unfiltered episodes on Youtube. www.youtube.com/channel/UCjiJPTpIJdIiukcq0UaMFsA
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