A Random-Effects Probit Model for Predicting Medical Malpractice Claims

Abstract

We use Oregon state data (1981-1990) on medical malpractice claims to develop a random-effects probit model for vulnerability to a medical malpractice claim in practice year k (k = 1, 2, …, n_i ) for physician i(i = 1, 2, …, N physicians in the sample) conditional on an n_i × p covariate matrix W_i that contains a mixture of p time-varying and time-invariant covariates. In this application, time-invariant covariates were physician sex and specialty (surgical versus nonsurgical). Time-varying covariates were age, the cumulative amount of risk management education (i.e., number of courses) taken by physician i to year k, and prior claim history. In addition, the model incorporates a random effect of “claim vulnerability” assumed to be normally distributed in the population of physicians. This random effect represents unobservable and/or unmeasured characteristics that place one physician at greater risk for experiencing a medical malpractice claim than another physician. In addition, we also determine if the effects of risk management training on claim vulnerability differ before and after the physician's first malpractice claim. Results of the analysis reveal that (1) there is a sizable random physician effect; (2) risk increases between age 40 to 60; (3) physicians in a surgical specialty are at increased risk; (4) male physicians are at greater risk than female physicians; (5) risk increases following an initial claim, particularly in the year subsequent to the initial claim, and (6) some beneficial effects of risk management education are observed in physicians with a prior claim history, particularly those in anesthesiology and obstetrics and gynecology.

Key Words:

• Actuarial statistics
• Binary data
• Longitudinal data
• Marginal maximum likelihood
• Risk management education