Objective

The overall objective is to establish guidance for emerging good practices in the application of machine learning methodology to traditional ISPOR methods, including economic evaluation, decision sciences and outcomes research in order to improve the value of healthcare delivery.

Specifically, this task force will: (1) introduce machine learning methods and their value in conducting research on health economics, as well as patient- and system-level outcomes research; (2) describe problems for which machine learning methods are appropriate. Particular attention will be devoted to four major use cases that HEOR scientists often face: the prediction of risk of various health care events; the causal estimation of treatment effects; developing models for economic evaluation; and model/data transparency.

Rationale

As demand for studies on the application of machine learning in healthcare is growing, so has the number of researchers who are conducting these studies and those using the findings from these studies. Researchers who conduct HEOR using machine learning methods come from diverse backgrounds and may lack basic training in the theory and methods for computer and data science. In addition, many of these researchers may not be aware of the range of machine learning methods available and the contexts in which they should be used most appropriately, recognizing both their strengths and limitations.

Co-Chairs:

Crown_Bill_2019William Crown, PhD
Chief Scientific Officer, Optum Labs,
Boston, MA, USA 

Padula HeadshotWilliam Padula, PhD
Assistant Professor of Pharmaceutical and Health Economics (USC), Leonard D. Schaeffer Center for Health Economics & Policy,
University of Southern California (USC),
Los Angeles, CA, USA

Leadership Group

Blythe Adamson PhD, MPH

Senior Quantitative Scientist, 
Flatiron Health

New York, NY, USA

Suzanne Belinson, PhD, MPH

Vice President, Commercial Markets, Tempus

Chicago, IL, USA 


Atul Butte MD, PhD

Priscilla Chan and Mark Zuckerberg Distinguished Professor and 
Director, 
Bakar Computational Health Sciences Institute

University of California – San Francisco (UCSF)

San Francisco, CA, USA

Federico Felizzi, PhD, MS

HTA Evidence Lead, F. Hoffman La Roche

Basel, Switzerland



Maarten J. IJzerman, PhD 

Professor, Clinical Epidemiology and Health Technology Assessment (HTA)

University of Melbourne

Melbourne, Australia

Noemi Kreif, PhD
Research Fellow, Centre for Health Economics
University of York
York, England, UK

Pall Jonsson, PhD
Associate Director, Research and Development

National Institute for Health and Care Excellence (NICE)

Manchester, England, UK

Juan-David Rueda, MD, MS 

Graduate Student Fellow, University of Maryland and 
Manager of Health Economics and Payer Evidence in Oncology
AstraZeneca Oncology
Baltimore, MD, USA

David Vanness, PhD

Professor of Health Policy and Administration
Pennsylvania State University
Hershey, PA, USA

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