Presentation: Electronic Health Data: Too Important to Be a Toy Example
2019-2020 Student-Invited Speaker: Sherri Rose, PhD, Associate Professor of Health Care Policy (Biostatistics), Harvard Medical School
Abstract: Health care is moving toward analytic systems that take large databases and estimate varying quantities of interest both quickly and robustly, incorporating advances from statistics, econometrics, and computer science. The massive size of the health care sector make data science applications in this space particularly salient for social policy. This presentation will discuss specific challenges related to developing and deploying statistical machine learning algorithms for health economics and outcomes research, including examples from the areas of health plan payment and medical devices. Considerations go beyond typical measures of statistical assessment, and include concepts such as dataset shift and algorithmic fairness. An overarching theme is that developing methodology tailored to specific substantive health problems and the associated electronic health data is critical given the stakes involved, rather than eschewing complexity in simplified scenarios that may no longer represent an actual real-world problem.