“Selection with Skills: Evidence from Radiologists” by Dr. David Chan
Dr. David Chan
Assistant Professor of Health Research and Policy
Center for Health Policy and Center for Primary Care and Outcomes Research
Stanford University
We study the problem of classification in the setting of radiology and connect it the economic literatures of selection and productivity. We develop a simple framework that uses the joint distribution of provider-specific decisions and outcomes to decompose variation in decisions and outcomes into skills and preferences. Radiologists vary in both their diagnostic rates (decision) and their false omission rates (outcomes), and radiologists with higher diagnostic rates have higher false omission rates. We rationalize these patterns with a model of diagnosis, in which radiologists with heterogenous diagnostic skill endogenously choose thresholds to minimize some function of false negatives and false positives. Radiologists wish to avoid false negatives more than false positives, and this imbalance increases with lower diagnostic skill. Variation in skills can explain 60% of variation in diagnostic decisions, and policies to improve skills generally improve welfare more than policies to reduce practice variation.
