Some Statistical Evidence on Merit Rating in Medical Malpractice Insurance

Abstract

Merit rating is not widely used in setting medical malpractice insurance premiums. A statistical analysis of two different data sets shows that actual malpractice claims experience is inconsistent with the notion that claims occur randomly among physicians within each specialty class. Consequently, a statistical model allowing physician specific claims propensities is fit to a recent data set. Calculations using this model indicate that the additional effect of four years of a physician's claims experience on his or her expected claims rate is comparable to the effect of knowing the physician's medical specialty. Consequently, merit rating deserves more serious attention in medical malpractice insurance. Merit rating is commonly used in many types of insurance (e.g., automobile insurance). By contrast, medical malpractice insurers do not. Premiums typically vary by medical specialty class and by large geographical region frequently by state. Recently there have been some physicianowned insurers that have used underwriting judgments based on a physician's individual experience in deciding whether to offer that physician insurance or to assess a surcharge, but this is the exception rather then the rule among medical malpractice insurers. The conventional view is summarized by the senior actuarial officer of a leading medical malpractice liability insurer who says there is no possibility of experience rating in the near future, because there is such a large chance element in malpractice. Most valid claims are produced by careful, responsible doctors who just make a mistake. [1O, p. 82]. The opposing view is expressed by the president of a doctor-owned mutual liability insurer who says we're now cancelling some doctors who have track records. We also charge some four and five times the normal premiums.' [4, p. 236]. If malpractice results from a bad apple problem, not using claims experience in setting premiums means that good doctors have to pay for the errors of John E. Rolph is a Senior Statistician with the Rand Corporation and the Institute of Civil Justice. He has a Ph.D. in statistics from the University of California, Berkley. Dr. Rolph has served on the faculty at Columbia University, the University of London, UCLA, and the Rand Graduate Institute. The author thanks Patricia Munch Danzon and William Schwartz, for interesting him in medical malpractice insurance and for helpful information and suggestions on an early draft of this paper. Comments by Edward Ignall, Emmett Keeler, and Charles Phelps are also gratefully acknowledged as is computing assistance from Bryant Mori. This work was supported by a grant from the then Department of Health, Education, and Welfare.