DIAGNOSING PHYSICIAN ERROR: A MACHINE LEARNING APPROACH TO LOW-VALUE HEALTH CARE.

ABSTRACT: How effective are physicians at diagnosing heart attacks? To answer this question, we contrast physician testing decisions with a machine learning model of risk. When the two deviate, we use actual health outcome data to judge whether the algorithm or the physician was right. We find physicians over-test: tests that are predictably useless are still performed. At the same time, physicians also under-test: many predicted high-risk patients are untested and then suffer adverse health events (including death) at high rates. A natural experiment using shift-to-shift testing variation confirms these findings: increasing testing improves health and reduces mortality, but only for patients flagged as high-risk by the algorithm. The simultaneous existence of over- and under-testing cannot easily b

SUBMITTER: Mullainathan S 

PROVIDER: S-EPMC12497445 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature