Identifying Malpractice‐Prone Physicians

We analyze the claims database of a large malpractice insurer covering more than 8,000 physicians and 9,300 claims. Applying empirical Bayes methods in a regression setting, we construct a predictor of each physician's underlying propensity to incur malpractice claims. Our explanatory factors are physician demographics (age, sex, specialty, training) and physician practice pattern characteristics (practice setting, procedures performed, practice intensity, special risk factors, and characteristics of hospital(s) on staff of). We divide physicians into medical and surgical/ancillary specialty categories and fit separate models to each. In the surgical/ancillary specialty group, physician characteristics can effectively distinguish between more and less claims‐prone physicians. Physician characteristics have somewhat less predictive power in the medical specialty group. As measured by predictive information, physician characteristics are superior to 10 years of claims history. Insofar as medical malpractice claims can be thought of as extreme indicators of poor‐quality care, this finding suggests that easily gathered physician characteristics can be helpful in designing targeted quality of care improvement policies.