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.
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.