Sun May 17
8:00 AM - 12:00 PM
Applied Generative AI for HEOR: Introduction
Topics: Methodological & Statistical Research
Level: Introductory
Separate registration required.
The rapid advancement in generative artificial intelligence (GenAI) presents an opportunity for transformative potential in the field of health economics and outcomes research (HEOR). This course provides an introductory understanding of generative AI models with a particular focus on large language models (LLMs), which are transforming the field of HEOR. Participants will be provided with an overview of the most appropriate ways to access LLMs, going beyond the use of chatbots. Further, they will be given insights into how to use prompt engineering, retrieval-augmented generation (RAG) and agents to conduct scientific research and gain an understanding on issues pertaining to privacy and security when using GenAI for HEOR. Participants will further explore specific applications of these models for conducting robust scientific HEOR research in, for example, systematic literature reviews (SLR) and economic evaluation. The course aims to equip participants with the knowledge to begin to use generative AI techniques for specific HEOR contexts and to appreciate how these innovative approaches can enhance HEOR activities. Practical exercises using Python and relevant AI frameworks will be incorporated for participants to follow along.
PREREQUISITES: Students should have a general understanding of common HEOR concepts such as SLRs and cost-effectiveness models. Knowledge of Python or similar programming languages such as R is considered a benefit but not required.
Speakers
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William Rawlinson
Estima Scientific, London, United Kingdom
Will is a senior health economist at Estima Scientific holding a degree in Physics and Philosophy from the University of Oxford. Will has 4 years’ experience developing cost-utility models and has specialized in applications of generative AI to health economic modelling. Will has published on the automation of R modelling using large language models (LLMs), and more recently has focused on applications of LLMs to Excel modelling and model reporting.
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Tim Reason, MSc
Estima Scientific, South Ruislip, United Kingdom
Tim Reason is co-founder of Estima Scientific and specializes in AI and evidence synthesis, having spent 15 years in the field of HEOR and technology. Tim is managing director of Estima, driving business activities, innovation and strategy for the company. Tim’s specializes in the intersection of HEOR, software development and AI to drive better outcomes for patients. Tim is the lead author on 2 seminal papers in AI for HEOR, showing that AI can be used to automate health economic modelling and NMA.
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Sven L Klijn, MSc
Bristol Myers Squibb, Utrecht, Netherlands
Sven Klijn is director at Bristol Myers Squibb in the Global HEOR Economic & Predictive Modeling group, where he leads the innovative modeling agenda in hematology and cell therapy. In addition, Sven has an active role in providing modeling education and masterclasses at international congresses. He has widely published on innovative methods, especially in the field of survival extrapolation and Generative AI. Sven has training in public health and health economics and previously had various roles in CROs related to health economic modeling.
Prompt Engineering for HEOR: Practical Skills and Use Cases for HEOR Professionals
Topics: Methodological & Statistical Research
Level: Introductory
Separate registration required.
Prompt engineering—the art and science of designing effective inputs for generative AI—has become a critical skill for health economists and outcomes researchers. Mastery of prompt engineering can significantly enhance productivity, accuracy, and innovation in HEOR, unlocking the full potential of large language models (LLMs) and other AI tools. This course delivers a comprehensive introduction to prompt engineering, tailored specifically for the HEOR context. Participants will gain hands-on experience with practical prompt strategies for systematic literature reviews (SLRs), economic modeling, real-world evidence generation, and more. The curriculum also addresses current best practices and common pitfalls, equipping attendees to confidently apply prompt engineering in regulated and high-stakes settings.
PREREQUISITE: Basic knowledge of systematic literature reviews and economic modeling will be helpful. No prior knowledge or use of AI is required.
Speakers
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Rachael Fleurence, MSc, PhD
Apodeixis Strategies, LLC, Boston, MA, United States
Rachael L. Fleurence, PhD, MSc, is the head of Evidence and AI Solutions, at Value Analytics Lab, a life sciences consultancy. A health economist by training, she previously served as senior advisor to Dr. Francis Collins at the National Institutes of Health, where she led a national initiative to eliminate Hepatitis C in the US. She also served as an advisor to the National Institute of Biomedical Imaging and Bioengineering (NIBIB), focusing on artificial intelligence and machine learning. Previously, Dr. Fleurence was a senior health policy advisor in the Biden-Harris White House and Senior Advisor to the NIH Director. She played a key role in the federal COVID-19 response, leading the “Say Yes! COVID Test” program and serving on White House pandemic policy groups. Prior to her federal service, she led the National Evaluation System for health Technology Coordinating Center (NESTcc) and PCORnet at PCORI and spent several years in the private sector in health economics and outcomes research (HEOR) consulting. Dr. Fleurence has received multiple NIH Director’s Awards, the HHS Secretary’s Award for Distinguished Service, and the National Champion for Global Hepatitis Elimination award. She co-led the ISPOR Task Force on EHR Data for Health Technology Assessment, serves on the ISPOR Working Group on Generative AI, and is an associate editor for Value in Health. She also sits on the boards of CTTI (the Clinical Trials Transformation Initiative) and ICN (the ImproveCareNow network). She holds degrees from Cambridge University, ESSEC Business School (Paris), and the University of York (UK).
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Jag Chhatwal, PhD
Harvard Medical School / Massachusetts General Hospital, Boston, MA, United States
Jag Chhatwal, PhD, is the director of the Institute for Technology Assessment at Massachusetts General Hospital and an associate professor at Harvard Medical School. He also serves as core faculty at the Center for Health Decision Science, Harvard T.H. Chan School of Public Health. Dr. Chhatwal has co-authored more than 125 original research articles and editorials in leading peer-reviewed journals. His research has informed health policy decisions at prominent organizations including the White House, the World Health Organization, and the CDC, and has been featured in major media outlets such as CNN, Forbes, National Public Radio, The New York Times, and The Wall Street Journal. Dr. Chhatwal serves as an associate editor of Value in Health and as guest editor for its special issue on artificial intelligence. He is also a member of the ISPOR Generative AI Working Group.
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Dalia Dawoud, BSc, MSc, PhD
Cytel Inc., London, United Kingdom
Dalia Dawoud, PhD, is Research Principal, HTA Policy and Strategy. She is also the Director and CEO of PEHTA Consulting Ltd. and holds a professor position at the Faculty of Pharmacy, Cairo University. She has over 15 years experience as a health economist and researcher. Her work is largely focused on the application of HEOR in HTA and clinical guideline development. She worked at leading organizations including NICE, where she led a portfolio of HORIZON Europe projects such as HTx, EDiHTA and SUSTAIN HTA, and the Royal College of Physicians, London. She is widely published in the areas of health economics and outcomes research and serves as associate editor for Value in Health and as director on ISPOR Board of Directors (2023-2026). She is also a member of the ISPOR AI Working Group and ISPOR Living HTA Working Group.
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Turgay Ayer, PhD
Value Analytics Labs, Boston, MA, United States
Turgay Ayer, PhD, holds the Virginia C. and Joseph C. Mello Chair and serves as the research director for Healthcare Analytics and Business Intelligence at the Center for Health & Humanitarian Systems at Georgia Tech. He is also the chief technology officer at Value Analytics Labs. Dr. Ayer holds a courtesy appointment at Emory Medical School where he teaches Big Data Analytics courses and serves as a Senior Scientist at the Centers for Disease Control and Prevention (CDC). Dr. Ayer’s research focuses on health economics modeling (HEOR), real-world evidence, data science, machine learning, econometric modeling, and healthcare analytics. He has published over 80 peer-reviewed journal papers and more than 300 conference abstracts, with his work featured in top-tier business, engineering, medical, and health policy journals. His research has attracted substantial attention from major media outlets, including The Wall Street Journal, The Washington Post, US News, and NPR. A recognized expert in HEOR, Dr. Ayer has been at the forefront of applying generative AI to navigate healthcare systems and support better decision-making. He has contributed significantly to the development of advanced models for predicting healthcare outcomes and designing innovative cost-effectiveness analysis frameworks. Under his leadership, Value Analytics Labs has focused on the development of cutting-edge technologies, including ValueGen.AI, to enhance healthcare analytics and improve the efficiency of healthcare decision-making processes.
Leveraging Real-World Data Throughout the Medical Devices and Diagnostics Product Lifecycle
Topics: Real World Data & Information Systems
Level: Intermediate
Separate registration required.
This course will focus on the opportunities and practical applications of conducting real-world data (RWD) studies and generating real-world evidence (RWE) for medical devices and diagnostics (MDD). RWD is increasingly being leveraged to support a variety of purposes in the MDD space, including regulatory, reimbursement, health technology assessment (HTA), and business needs. Leveraging RWD, especially secondary data sources, for MDD poses unique challenges, such as the difficulty in identifying devices in RWD sources, device operator characteristics potentially influencing outcomes, and the need to consider continuous device iterations in RWE generation. Thus, high-quality data and carefully designed studies are critical to increase the credibility and acceptance of RWD/E for MDD.
Additionally, the course will provide an overview of the best practices, processes, and methods to design and execute studies to gather market insights and generate high-quality evidence for multiple stakeholders. The course will review different types of secondary data sources and methods to conduct descriptive analyses and comparative effectiveness research along the MDD product lifecycle. Specific topics will include the common questions that are answered with secondary data and the challenges and potential solutions that are unique to MDD products. Case studies will focus on a variety of technologies, from new technologies to follower products, and the strategies that are used to increase the chances of acceptance to gain and expand market access.
PREREQUISITES: Participants should be familiar with general HEOR methods and tools used in MDD, and the general concepts of real-world data and evidence, including the types of healthcare data that is generated as part of routine healthcare.
Speakers
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Belinda A Mohr, PhD
Medtronic, Phoenix, AZ, United States
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Arthi Chandran, MPH, MS, DrPH
ABBOTT, Santa Clara, CA, United States
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Bijan J Borah, MSc, PhD
Mayo Clinic College of Medicine, Edina, MN, United States
Causal Inference and Causal Diagrams in Big, Real-World Observational Data and Pragmatic Trials
Topics: Real World Data & Information Systems
Level: Advanced
Separate registration required.
Innovative causal inference and target trial emulation methods are needed for the design and analysis of big real-world observational data and pragmatic trials. This course will introduce the principles of causation in comparative effectiveness research, the use of causal diagrams (directed acyclic graphs; DAGs) and focus on causal inference methods for time-independent confounding (multivariate regression, propensity scores) and time-dependent confounding (g-formula, marginal structural models with inverse probability of treatment weighting, and structural nested models with g-estimation). The “target trial” concept and a counterfactual approach with “replicates” will be used to apply causal methods to big real-world datasets with case examples from oncology, cardiovascular disease, HIV, nutrition and obstetrics. The course will consist of lectures, case examples drawn from the published literature and interactive discussion. The intended audience includes researchers from all substance matter fields, statisticians, epidemiologists, outcome researchers, health economists and health policy decision makers interested either in methods of causal analysis or causal interpretation of results based on the underlying method.
PREREQUISITE: Students are expected to have a basic knowledge in epidemiologic studies and methods (including the concept of confounding).
Speakers
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Uwe Siebert, MPH, MSc, ScD, MD
UMIT TIROL - University for Health Sciences and Technology; Harvard Chan School of Public Health, Hall in Tirol, Austria
Uwe Siebert, MD, MPH, MSc, ScD, is a professor of Public Health, Medical Decision Making and Health Technology Assessment (HTA), chair of the Department of Public Health, Health Services Research and HTA at UMIT TIROL-University for Health Sciences and Technology in Austria and director of the Division for HTA in the ONCOTYROL–Center for Personalized Cancer Medicine in Austria. He is also adjunct professor of Epidemiology and Health Policy & Management at the Harvard T.H. Chan School of Public Health and Affiliated Researcher in the Program on Cardiovascular Research at the Institute for Technology Assessment and Department of Radiology at the Massachusetts General Hospital, Harvard Medical School, Boston.
After medical school, he worked for several years as a physician in international public health projects in West Africa, Brazil, and Germany. He then earned an MPH at the Munich School of Public Health and completed an MSc in Epidemiology and a ScD in Health Policy and Management with a concentration in decision sciences at the Harvard School of Public Health.
His research interests include applying real-world evidence-based quantitative, causal and translational methods from public health, epidemiology, artificial intelligence, comparative effectiveness research, health services and outcomes research, economic evaluation, modeling, and health data a d decision science in the framework of health care policy advice and HTA as well as in the clinical context of routine health care, clinical guideline development, public health policies and patient guidance. His research focuses on cancer, infectious disease, cardiovascular disease, neurological disorders, and others.
He has been leading projects/work packages in several EU FP7, H2020 and Horizon Europe projects (eg, ELSA-GEN, BiomarCaRE, MedTecHTA, DEXHELPP, EUthyroid, FORECEE, MDS-RIGHT, RECETAS, CORE-MD, EUREGIO-EFH, CIDS, OnCoVID, 4D PICTURE, CATALYSE). He teaches HTA, health economics, modeling, epidemiology, causal inference and target trial emulation, and data and decision science for academia, industry, and health authorities in Europe, North and South America, and Asia. He directs the Continuing Education Program on Health Technology Assessment & Decision Sciences (htads.org).
He has served as member of the ISPOR Directors Board and as president of the Society for Medical Decision Making (SMDM). He is a leadership member of the ISPOR Personalized/Precision Medicine SIG, a member of the Latin America Consortium Advisory Committee of ISPOR, and co-chair of the ISPOR-SMDM Modeling Good Research Practices Task Force. He is a member of the Oncology Advisory Council and the National Committee for Cancer Screening of the Austrian Federal Ministry of Health.
He has authored more than 400 publications (> 30,000 citations, H index > 80), and is editor of the European Journal of Epidemiology. Further information Internet: http://htads.org, umit-tirol.at/dph, hsph.harvard.edu/uwe-siebert, Twitter: @UweSiebert9, LinkedIn: uwe-siebert9.
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Douglas Faries, PhD
Consultant, Alma, AR, United States
Doug Faries has a PhD in Statistics from Oklahoma State University. He spent 34 years as a statistician in the pharmaceutical industry, retiring as a vice president of Real-World Access and Analytics at Eli Lilly and Company where he led the development of real-world analytical capabilities for the business. Doug has extensive experience with the design and analysis of observational research including comparative effectiveness analyses and sensitivity analysis for unmeasured confounding. He remains active in the statistical community with over 150 peer-reviewed manuscripts, authoring books and book chapters on analysis of observational data, and teaching short courses on causal inference at national meetings.
Designing a Patient-Centered Strategy for Drug Development and Value
Topics: Patient-Centered Research
Level: Intermediate
Separate registration required.
This course focuses on how to design and govern a fit-for-purpose patient-centered strategy before instruments, endpoints, or implementation approaches are locked into a clinical program. Rather than teaching how to execute a specific measurement system, the course equips participants to make informed, defensible decisions about whether, when, and how tools such as patient-reported outcomes should be used to support patient-centered evidence generation across development, regulatory review, reimbursement, and value communication. The course will provide an overview of where and how patient-centered evidence can provide value to decision makers across the drug development lifecycle.
Participants will learn structured frameworks for identifying what aspects matter most to patients and other decision makers, identifying available measures and other ways to gather patient-centered information, assessing the strengths and limitations of existing instruments, determining when adaptation or new development may be warranted, and how to develop a measurement strategy to inform multiple decision makers. The course emphasizes critical judgment over mechanics, helping participants understand how qualitative and quantitative evidence expectations vary by decision maker (patients, clinicians, regulators, HTA bodies, payers) and how these differences influence early strategy choices.
Through interactive discussion and real-world examples, the course explores common strategic missteps—such as misaligned endpoints, overambitious claims, or inappropriate instrument selection—that can undermine downstream clinical, regulatory, or access objectives. Success stories are used to illustrate how strong upfront strategy development enables smoother execution later. By the end of the course, participants will be prepared to lead patient-centered strategy conversations, challenge assumptions, align cross-functional decision makers, and set realistic objectives—whether the next step is selecting an existing measurement system, adapting a legacy instrument, or deciding not to pursue patient-reported outcome endpoints at all.
PREREQUISITE: This course assumes that participants will have a basic knowledge of key PRO-related concepts (eg, health-related quality of life, symptoms, impacts, a general knowledge of the PRO development steps, and a working knowledge of PRO measurement within clinical programs)
Speakers
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Ari Gnanasakthy, MBA, MSc
RTI- Health Solutions, RESEARCH TRIANGLE PARK, NC, United States
Ari Gnanasakthy is head of Patient-Reported Outcomes at RTI-HS. Prior to RTI-HS.
Mr. Gnanasakthy was the executive director and head of the Patient-Reported Outcomes Center of Excellence at Novartis Pharmaceuticals. He has almost 25 years of experience in the pharmaceutical industry. At Novartis, he worked in several departments, including Biostatistics, Health Economics, Pricing, and Outcomes Research. After receiving his bachelor's degree in mathematics, statistics, and computing, Mr. Gnanasakthy joined Rothamsted Experimental Station (UK), where he was responsible for the statistical analysis of survey data of agricultural soil in England and Wales. He then joined the Milk Marketing Board (UK), where he was a part of the team responsible for modeling lactation curves of dairy cows. Mr. Gnanasakthy's extensive experience in the field of statistics and outcome research has resulted in numerous abstracts and almost 40 publications. Throughout his career, Mr. Gnanasakthy has developed and validated over a dozen patient-reported outcomes instruments and currently serves in the editorial board of Cancer Clinical Trials and a reviewer for many professional journals, including Value in Health.
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Rebecca Crawford, MA
RTI Health Solutions, Manchester, United Kingdom
Ms. Crawford has 13 years of experience providing consultative support to pharmaceutical companies with a focus on the development of patient-reported outcome (PRO) measurement strategies to best meet the needs of their clinical trial programs.
Ms. Crawford has developed, culturally adapted, and validated clinical outcome assessment measures, including PROs for several different therapeutic areas. Ms. Crawford has expertise in research design and in the application of both traditional and innovative qualitative research methods, including the collection and analysis of social media data to provide insights into the patient disease and treatment experience.
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Shanshan Qin, PhD
RTI- Health Solutions, Research Triangle Park, NC, United States
Shanshan Qin, PhD, received her training on Qualitative Methodology (including statistic inference and estimation, traditional and modern testing theories, structural equation modeling, and mixed and mixture modeling) at University of Georgia. She has 10 years of experience as a psychometric analyst and statistic consultant and has been working on psychometric evaluation of clinical outcome assessments (COAs) and support of regulatory review of COA labeling claims in various therapeutic areas, including dermatology, gastroenterology, diabetes, oncology, ophthalmology, and mental disorder. She is an experienced programmer of SAS, R, and IRT PRO.
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Lynda Doward, MSc
RTI- Health Solutions, Manchester, United Kingdom
Ms. Doward has over 30 years of experience conducting patient-centered outcomes research including the provision of strategic advice to pharmaceutical companies in the incorporation of the patient voice into drug development programs. Ms. Doward is an expert in the development of clinical outcome assessment (COA) strategies including the development of patient-centered clinical trial endpoints, the implementation of patient-reported and other COA outcome measures in clinical trial programs, and the inclusion of PRO and other COA value messages at key drug development hurdles. Ms. Doward has extensive experience in supporting pharmaceutical clients in their COA-related submissions to regulatory agencies in Europe and the US and advises on health-utility measurement strategies for reimbursement agencies in Europe. Ms. Doward has led the development of over 40 COA questionnaires that have been adapted and validated for use in over 60 languages worldwide.
Ms. Doward currently serves on the ISPOR COA Special Interest Group (leadership committee) and the ISPOR Patient Council (member) and was a member of the leadership committee of the completed ISPOR Good Research Practices Task Force for the measurement of health state utilities in clinical trials. Ms. Doward has acted as a consultant to the World Health Organization and has served as a Research Advisor to the UK Department of Health, and medical charities in the United Kingdom.
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Diane Whalley, PhD
RTI- Health Solutions, Manchester, United Kingdom
8:00 AM - 3:30 PM
Budget Impact Analysis in Practice: A Hands-On Course on Strategic Conceptual Design, Model Building, and Communication
Topics: Economic Evaluation
Level: Intermediate
Separate registration required.
This hands-on, interactive course equips participants with the conceptual and practical tools needed to develop, adapt, and communicate budget impact analyses (BIA) for real-world decision making.
The course begins with a brief review of BIA concepts and a 6-step approach to strategically design these analyses. We will discuss practical applications, including customization to accommodate payer-specific data; balance of structural simplicity, accuracy, and face validity; interpretation of results; and development and communication of compelling value messages. Technical topics will include static versus dynamic budget impact models, good Excel model-building practices, considerations for device and diagnostic technologies, and incorporation of realistic features such as patient copayments and availability of generics.
Instructors will walk through 2 budget impact models programmed in Excel (1 static and 1 dynamic) and will work with participants during hands-on exercises to customize and adapt these models to specific real-world circumstances. These Excel-based models will be provided to participants in advance, both for use during the session and for later reference. Throughout the course, participants will have opportunities to network and to work in small groups to discuss key concepts, plan and implement model adaptations, and develop and communicate value narratives.
This course is designed for participants seeking to deepen their understanding of BIA, gain practical exposure to Excel-based models, and strengthen their ability to communicate BIA in ways that influence decision making. Registrants who wish to participate in the interactive portions of the course will need to bring materials for handwrtten activities.
PREREQUISITE: Participants are expected to have familiarity with basic budget-impact analysis concepts and working knowledge of Microsoft Excel. Individuals who need foundational knowledge of budget impact models are encouraged to register for the introductory level virtual short course “Primer on a 6-Step Approach to Budget Impact Analysis” on March 25, 2026. A microcourse on the basics of Budget Impact Analysis is available in the ISPOR Education Center.
Speakers
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Stephanie Earnshaw, PhD
Access Strategy Consulting, Pittsboro, NC, United States
Stephanie Earnshaw has performed health outcomes and health services research for 30+ years. Her research focuses on applying decision-analysis techniques (eg, decision trees, Markov processes, simulation) to industry-related issues and health care problems. In addition to developing budget-impact and cost-effectiveness models to support health technologies for the pharmaceutical, biotechnology, and diagnostic and medical device industry, she has developed innovative mathematical models using these methods to determine pricing strategy, predict clinical outcomes, allocate resources, and cost care pathways particularly in support of medical diagnostics.
Dr. Earnshaw received her PhD in Industrial Engineering at North Carolina State University and is a member of ISPOR and the Institute for Operations Research and Management Sciences. She has presented her work at professional conferences and has published in several peer-reviewed journals. She has presented workshops and various courses on decision-analytic modeling techniques for pharmaceutical companies and organizations such as ISPOR, the Academy of Managed Care Pharmacy (AMCP), and the Centers for Disease Control and Prevention (CDC). Dr. Earnshaw has served on the ISPOR Board of Directors and as Chair of the Audit Committee and Educational Council. She has held an Adjunct Faculty appointment at the University of North Carolina’s Eshelman School of Pharmacy, Division of Pharmaceutical Outcomes and Policy, is honored as a Distinguished Alumni in Industrial and Systems Engineering at North Carolina State University and is one of the lead authors of “Budget-Impact Analysis of Health Care Interventions: A Practical Guide.”
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Anita Brogan, MSc, PhD
AESARA, San Diego, CA, United States
Anita Brogan is vice President of Value Evidence at AESARA, Inc. In her role, she uses analytical techniques to assess and communicate the health and economic impact of emerging pharmaceutical and biotechnology interventions. She leads development of user-friendly and transparent cost-effectiveness, budget-impact, optimization, population, and other models programmed in Microsoft Excel and other platforms. She has expertise in modeling methodologies such as Markov and other stochastic models, infectious disease dynamic transmission models, simulation, regression, linear and nonlinear programming, and various types of sensitivity analysis. Dr. Brogan has developed models and analyses in the areas of HIV, hepatitis C, RSV, norovirus, Ebola, influenza, cystic fibrosis, bone health, mental health, women’s health, oncology, chronic pain, age-related macular degeneration, hospital-acquired infection, financial portfolio optimization, and vehicle routing. Her research has been presented at various professional conferences and published in peer-reviewed journals.
Dr. Brogan holds a PhD in Operations Research from the University of North Carolina at Chapel Hill. She serves on editorial board of Pharmacoeconomics and the ISPOR education council. She has presented workshops and short courses on decision-analytic modeling techniques in a variety of venues, including meetings of ISPOR and the Academy of Managed Care Pharmacy (AMCP). She is co-author of the book “Budget-Impact Analysis of Health Care Interventions: A Practical Guide.”
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Ashley Davis, PhD
RTI Health Solutions, Research Triangle Park, NC, United States
Ashley Davis is a senior director within the Health Economics division at RTI Health Solutions (RTI-HS). She holds a PhD in Industrial Engineering and Management Sciences from Northwestern University and has been with RTI- HS for 10 years. She has presented short courses on budget impact modeling techniques in a variety of venues, including meetings of The Professional Society for Health Economics and Outcomes Research (ISPOR).
In her role at RTI-HS, Dr. Davis uses analytical methodologies to evaluate the clinical and economic value of upcoming pharmaceutical products and changes to healthcare policies. She has developed user-friendly and transparent cost- effectiveness, cost-utility, and cost-consequence models; budget-impact models; resource allocation models; and infectious disease transmission models programmed in Microsoft Excel and other platforms. She has experience with numerous types of modeling techniques, including Markov and other stochastic models, simulation, statistical analysis, linear and nonlinear programming, robust optimization, and various types of sensitivity analysis. Dr. Davis has developed models and analyses in the areas of HIV, hepatitis C, cystic fibrosis, herpes zoster, influenza, pneumococcal disease, respiratory syncytial virus, severe asthma, chronic obstructive pulmonary disease, eosinophilic esophagitis, spinal surgery, cytomegalovirus, and organ transplantation. Her research has been presented at various professional conferences and published in peer- reviewed journals.
1:00 PM - 5:00 PM
Applied Generative AI for HEOR: Advanced Architectures
Topics: Methodological & Statistical Research
Level: Intermediate
Separate registration required.
Generative AI (GenAI) is rapidly transforming how health economics and outcomes research (HEOR) is conducted from literature reviews and evidence synthesis to economic modeling and HTA submissions. As the field moves beyond experimentation, professionals face a new challenge: how to responsibly validate, implement, and scale GenAI solutions in real-world HEOR settings.
This intermediate-level course builds upon basic concepts and is designed for HEOR professionals, data scientists, and decision makers seeking to understand not only how GenAI works, but how to implement and evaluate it effectively within regulated and evidence-driven environments. The course provides a practical framework for moving “from prototype to practice,” describing the lifecycle of GenAI implementation—from early sprints and pilot projects to production deployment. Participants will explore both technical and organizational perspectives, including workflow orchestration, modularization, scaling, and change management.
Retrieval-Augmented Generation (RAG) is a cornerstone architecture that integrates external knowledge bases into LLM workflows. Faculty will discuss why RAG is particularly relevant for HEOR, demonstrating how external information (eg, clinical data, published evidence, HTA guidance) can be incorporated in GenAI workflows according to best practice standards and used to improve factual accuracy and traceability. A guided practical session is included so participants become familiar with how to implement a simple RAG pipeline, learning how to chunk data, generate embeddings, and augment prompts for domain-specific use. The course will also provide an extensive overview of Agentic AI, a fast-evolving frontier in AI automation. Participants will examine how autonomous AI “agents” can coordinate multi-step HEOR processes—such as literature updates, model maintenance, or simulated committee reviews—while maintaining control and accountability. A second practical session will demonstrate an agentic workflow in action, showcasing task orchestration, monitoring, and boundary setting. Beyond technical topics, there will be a focus on evaluation and validation of GenAI solutions for HEOR, where participants will learn how to critically assess GenAI tools in terms of reliability, reproducibility, and regulatory alignment. This will also be discussed in the context of potential ethical concerns around the application of AI. Using frameworks such as ELEVATE-GenAI, and referencing NICE and FDA guidance, participants will learn how to ensure that AI-driven outputs meet HEOR’s high standards for quality and transparency. By the end of this course, participants will understand how to bridge the gap between exploratory AI use and operational excellence. They will leave with actionable frameworks and hands-on knowledge to evaluate, implement, and govern GenAI tools that enhance productivity, transparency, and scientific integrity across HEOR activities.
PREREQUISITES: Completion of the “Applied Generative AI for HEOR: Introduction” ISPOR course or familiarity with concepts such as prompt engineering, APIs, and LLM workflows. A basic understanding of Python or other similar scripting languages is recommended to get the most benefit from the guided practical sessions.
Speakers
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Sven L Klijn, MSc
Bristol Myers Squibb, Utrecht, Netherlands
Sven Klijn is director at Bristol Myers Squibb in the Global HEOR Economic & Predictive Modeling group, where he leads the innovative modeling agenda in hematology and cell therapy. In addition, Sven has an active role in providing modeling education and masterclasses at international congresses. He has widely published on innovative methods, especially in the field of survival extrapolation and Generative AI. Sven has training in public health and health economics and previously had various roles in CROs related to health economic modeling.
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Rajdeep Kaur, PhD
Pharmacoevidence Pvt. Ltd., Mohali, India
Dr. Rajdeep Kaur is the Lead of AI Sciences at Pharmacoevidence, with a Ph.D. in Computer Science and Engineering and 17+ years of expertise in advanced technologies. Her work focuses on Generative AI, machine learning, and cloud-enabled data systems, with a strong emphasis on real-world healthcare applications. She has successfully led multiple GenAI projects, combining deep technical expertise to deliver impactful AI-driven solutions.
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Ghayath Janoudi, MD, PhD
Loon, Ottawa, ON, Canada
Dr. Ghayath Janoudi, MBBS, MSc, PhD, is the Founder and CEO of Loon, an AI-driven clinical research and market access company developing scientifically validated AI agents for Health Economics and Outcomes Research (HEOR), Health Technology Assessment (HTA), and reimbursement strategy.
A medical doctor and health outcomes researcher by training, Dr. Janoudi holds a PhD in Clinical Epidemiology with a specialization in artificial intelligence for clinical research. He previously held senior leadership roles at Canada’s Drug Agency (formerly CADTH) and at clinical research organizations, where he led work on HTA, drug reimbursement policy, and value evidence evaluation.
A recognized thought leader in AI for clinical discovery, Dr. Janoudi is a well-published author in AI-enabled evidence synthesis, and was named Canada’s 2024 Emerging Healthcare Leader for his contributions to accelerating timely and equitable access to innovative therapies.
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Siguroli Teitsson, BSc, MSc
Bristol Myers Squibb, Denham, United Kingdom
Siguroli Teitsson is a Director in Global HEOR Economic & Predictive Modeling at Bristol Myers Squibb. In his role, Siguroli leads the advancement of innovative modeling and analytics in oncology, and drives the integration of cutting-edge AI automations in HEOR and market access, streamlining workflows to accelerate patient access to medicines. With a background in engineering and health economics, he has previously held senior roles in CROs and has extensive publication record in innovative analytics within the field of HEOR, contributing to advancements in methodology and practice.
Developing Decision-Grade Real-World Evidence
Topics: Real World Data & Information Systems
Level: Intermediate
Separate registration required.
In this course, participants will be introduced to the principles of what makes real-world evidence (RWE) decision-grade, including an extended example. In the first half of the course, we will review the most recent RWE frameworks and guidelines and examine case studies in which RWE was used in regulatory and HTA approval. The second half of the course is an extended example in which participants will examine a study that could support an indication expansion and interactively discuss how choices made in the design and implementation may affect the meaning and interpretability of results.
PREREQUISITE: Students are expected to be familiar with relevant concepts and methodologies for analyzing real-world data.
Speakers
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Sebastian Schneeweiss, ScD, MD
Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States
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Jeremy Rassen, ScD
Aetion, Inc., New York, NY, United States
Jeremy A. Rassen, MS, ScD is a pharmacoepidemiologist with 25 years of academic and industry experience. He is cofounder, president, and chief technology officer at Aetion, a healthcare technology company that delivers real-world evidence for life sciences companies, payers, and regulatory agencies. Prior to founding Aetion, Dr. Rassen was assistant professor of medicine at Harvard Medical School, where he focused on methods to improve the quality and validity of real-world data studies. He also worked in Silicon Valley in a variety of tech companies. Dr. Rassen received his bachelor’s degree in computer science from Harvard College and his master’s and doctorate degrees in Epidemiology from the Harvard TH Chan School of Public Health.
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Shirley Wang, PhD
Brigham & Women's Hospital, Harvard Medical School, Boston, MA, United States
Dr. Wang is an associate professor at Brigham and Women’s Hospital, Harvard Medical School and lead epidemiologist for the Food and Drug Administration's (FDA) Sentinel Innovation Center. She leads the Meta-Research in Pharmacoepidemiology program, with recent projects aimed at improving the transparency, reproducibility, and robustness of evidence from healthcare databases (www.repeatinitiative.org) and informing when and how real-world evidence studies can draw causal conclusions to inform regulatory or other healthcare decision-making (www.rctduplicate.org). She is currently PI on multiple NIH R01s and is also funded by FDA. Her methods work has received 3 awards from international societies.
Using RWE to Inform the Value and Affordability Assessment of Cell and Gene Therapies
Topics: Real World Data & Information Systems
Level: Intermediate
Separate registration required.
This short course explores the role of real-world evidence (RWE) in supporting the economic evaluation of cell and gene therapies (CGTs). Many CGTs are one-time therapies that have the potential to offer transformative sustained benefits for patients with severe conditions. In many situations, the ‘do-nothing’ alternative is the norm, so the need for a control arm may be futile yet reducing the options for patients to be included in allegedly active arms. However, at launch there is often resistance from payers to reimburse these potentially transformative therapies due to the limited validity of the supporting evidence (small, single arm trials, etc) which leads to uncertainty regarding: the size and heterogeneity of the patient population eligible for CGTs; the definition of standard of care and natural disease progression, given CGTs may unlock treatment possibilities of previously deemed untreatable and rare diseases; the novel therapy’s duration of benefit; and the relative effectiveness of the novel therapy compared to the current standard of care, particularly, as cell and gene therapies are often only supported by a single arm trial.
Payer concerns with these uncertainties in the evidence are heightened by the typically high up-front costs associated with cell and gene therapies and the consequence for their affordability. While there is generally not a good understanding of the effect sizes and costs of standard of care. This course will provide an overview of the potential contribution, planning and use of RWE to help address these concerns. We will assume payer archetypes that are focused on one or more of relative effectiveness and cost-effectiveness (value), and budget impact (affordability). Within these archetypes, we will discuss the role and acceptability of RWE to inform payment and policy with emphasis on eligibility, appropriate comparators, durability of benefit, spending for patients that meet criteria for eligibility and the development of appropriate outcome-based agreements. A strong focus will be brought on validity (internal and external) of the existing data. We will further provide participants with real-world examples of using RWE to inform policy making.
This course will target a wide range of participants, from medical operations to HEOR, interested in understanding the depth of issues to consider when balancing access with payment for CGTs.
PREREQUISITE: This course requires familiarity with basic economic evaluation and HTA concepts and methodologies of pharmaceuticals.
Speakers
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Daniel Gladwell, BA, MSc, PhD
Lumanity, Sheffield, United Kingdom
Dan Gladwell is the chief scientific officer for Lumanity HE&HTA. A health economist by background, he has a particular passion for demonstrating the value of highly innovative therapies that make a transformative difference to patient outcomes. Dan supported one of the first CAR T-cell therapies to be assessed by NICE. Since that appraisal Dan has been continuously engaged in supporting patient access to cell and gene therapies through engaging in HEOR evidence generation planning efforts at the Global, Regional and National levels; and informing efforts to shape the HTA policy context for cell and gene therapies.
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Antal T Zemplenyi, MSc, PhD
University of Colorado Skaggs School of Pharmacy and Pharmaceutical Science, Denver, CO, United States
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Oriol de Sola-Morales, MSc, PhD, MD
Fundacio HiTT, Barcelona, Spain
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Renske MT ten Ham, PhD
Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Amsterdam, Netherlands
PROs in Clinical Trials: Endpoint Selection, Regulatory Strategy and Label Claims
Topics: Patient-Centered Research
Level: Advanced
Separate registration required.
This short course shows clinical development teams how to implement PROMIS® (Patient-Reported Outcomes Measurement Information System) in registration trials and regulatory submissions, using FDA Patient-Focused Drug Development guidance. Participants learn regulator-aligned methods applicable to other PRO measures, from endpoint selection to labeling claims. PROMIS provides comprehensive health assessment across physical, mental, and social domains using IRT-based scoring, computer-adaptive testing, digital capabilities, and validation across 80+ languages. The course covers domain/measure selection, protocol specification, estimands, meaningful change determination, psychometric evaluation, digital ePRO implementation, global translations, and regulatory review preparation. Interactive case-based learning uses real disease examples to guide participants through endpoint selection, protocol development, and regulatory defense. Faculty analyze successful label claims and challenging submissions to identify critical success factors and common pitfalls applicable across PRO instruments, including real-world evidence and HTA applications. A laptop is recommended for interactive exercises and accessing online resources.
PREREQUISITES: Participants should have basic familiarity with clinical trials and patient-reported outcomes, clinical trial design and regulatory processes. Prior PROMIS® knowledge is not required.
Speakers
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David Cella, PhD
Northwestern University, Chicago, IL, United States
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Maja Kuharic, PhD
Northwestern University Feinberg School of Medicine, CHICAGO, IL, United States
Maja Kuharic, MPharm, MSc, PhD, is an Assistant Professor in the Department of Medical Social Sciences at Northwestern University Feinberg School of Medicine. Her research focuses on patient-reported outcomes (PROs), health-related quality of life, and economic evaluation. She specializes in the development, validation, and application of PROMIS measures and preference-based instruments such as the EQ-5D and EQ-HWB.
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Xiaodan Tang, PhD
Northwestern University, Chicago, IL, United States
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Heather Gelhorn, PhD
PPD Evidera Patient-Centered Research, Thermo Fisher Scientific, Winter Park, CO, United States
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Selena Daniels, PharmD, PhD
US Food and Drug Administration, Silver Spring, MD, United States
Dr. Selena Daniels serves as the Deputy Division Director in the Division of Clinical Outcome Assessment at the FDA. She provides direction, oversight, and leadership to a team of expert analysts who provide consultation and advice on clinical outcome assessment (COA) endpoint development and validation, including considerations for clinical trial design, conduct, analysis, interpretation, and reporting for regulatory determinations of medical product benefit.
Prior to joining the FDA in 2015, Dr. Daniels worked in the Health Economic and Outcomes Research (HEOR) group at Allergan, Inc (now Abbvie), where she developed and executed HEOR strategies, as well as developed and implemented innovative COA strategies and endpoints for clinical trials.
Dr. Daniels received her Doctor of Philosophy degree in Education at Nova Southeastern University and Doctor of Pharmacy degree at Loma Linda University.
Societal Valuation of Innovative Medicines: Emphasis on Investment Perspective and Orphan Disease
Topics: Health Policy & Regulatory
Level: Intermediate
Separate registration required.
Explore the value assessment of innovative drugs from the perspectives of relevant stakeholders, their respective data requirements, and their methods and processes. Gain a better understanding of the value assessment from the investor perspective, with a focus on orphan drugs and advanced therapy medical products (ATMPs). The value of medical innovation depends on a stakeholder's perspective in different decision contexts. Regulatory authorities (EMA, FDA) mainly consider the clinical value of medical innovation. In the context of coverage decisions, national health authorities may adopt a broader perspective by including clinical, economic criteria, and sometimes even other criteria such as equity and social values. For pricing and reimbursement, ""value-based pricing"" is the most widely accepted approach across countries, but it can vary from a narrow concept based on the incremental cost-effectiveness ratio (ICER) threshold to broader societal or holistic approaches.
Value-based pricing determines the maximum price from the national payer perspective. In the context of the investment decision, this price should exceed the minimum price for the investor acting in the international financial market to make a financial valuation. Furthermore, there are numerous other stakeholders, eg, patients, physicians, healthcare insurers, and employers--with their specific assessment of the value of medical innovation including, for example, patient and family quality of life, real-world effectiveness, budget impact, and the costs of lost productivity. Familiarity with health economic evaluation is desirable, but the course assumes little or no familiarity with economic valuation theory.
Speakers
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Lou Garrison, PhD
The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, Seattle, WA, United States
Lou Garrison, PhD, is professor emeritus in The Comparative Health Outcomes, Policy, and Economics Institute in the School of Pharmacy at the University of Washington, where he joined the faculty in 2004.
For the first 13 years of his career, Dr. Garrison worked in non-profit health policy at Battelle and then the Project HOPE Center for Health Affairs, where he was the Director from 1989-1992. Following this, he worked as an economist in the pharmaceutical industry for 12 years. From 2002-2004, he was vice president and head of Health Economics & Strategic Pricing in Roche Pharmaceuticals, based in Basel, Switzerland.
Dr. Garrison received a BA in Economics from Indiana University, and a PhD in Economics from Stanford University. He has more than 150 publications in peer-reviewed journals. His research interests include national and international health policy issues related to personalized medicine, benefit-risk analysis, and other topics, as well as the economic evaluation of pharmaceuticals, diagnostics, and other technologies.
Dr. Garrison was elected as ISPOR President for July 2016-June 2017, following other leadership roles since 2005. He recently co-chaired the ISPOR Special Task Force on US Value Frameworks. He was selected in 2017 by PharmaVOICE as being among “100 of the Most Inspiring People” in the industry. He recently received the PhRMA Foundation and Personalized Medicine Coalition 2018 Value Assessment Challenge First-Prize Award as lead author on a paper on “A Strategy to Support the Efficient Development and Use of Innovations in Personalized and Precision Medicine.”
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Marlene Gyldmark, MPhil
Beigene, Basel, Switzerland
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Mark J Nuijten, MBA, PhD, MD
A2M - Minerva, bergen op zoom, Netherlands
Mark Nuijten is a medical doctor, health economist, valuation economist, and healthcare publicist. He is a visiting professor at Ben-Gurion University in Israel, setting up the department on Clinical and Economic Valuation of Medical Innovation. He has become a leading health policy and economics expert over the last 2 decades, reflected in more than 200 publications and leading positions in scientific societies and editorial boards. Dr. Nuijten was board director of ISPOR (2002-2004) and chair of the Management Board of Value in Health (2002-2004). He was a member of the Editorial Advisory Board of Value in Health. He obtained his PhD in health economics (2003) on the thesis “In search for more confidence in health economic modelling” at the Erasmus University, Rotterdam.
Mark Nuijten is founder of A2M (Ars Accessus Medica) and founding partner of the Minerva International Health Economic Network. He was trained as a physician and worked in clinical research before obtaining his international MBA from Erasmus University, Rotterdam, where he later was a senior staff member. Prior to setting up Ars Accessus Medica, Dr. Nuijten was the founding managing director of the IQVIA Quintiles office in the Netherlands, which included European responsibility for the policy and health economic division.
He is a pioneer in the field of healthcare innovation in biotechnology and has been the first classical health economist successfully applying and developing Discounted Cash Flow methodologies for valuation of biotechnology innovation (eg, a pricing model to assess prices of expensive orphan drugs from an investor’s perspective—published in a Nature journal). He also developed an integrated valuation model, an interactive dynamic tool for the economic valuation of R&D projects, which can be used to optimize the initial clinical program (eg, indication, comparator, outcomes, and study design), and the associated pricing and market access pricing strategy.
