The Use of Patient Satisfaction Surveys and Alternative Coding Procedures to Predict Malpractice Risk

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Abstract
Because costs associated with malpractice litigation draw substantial resources away from patient care, many health care organizations are seeking efficient methods to manage these risks. The purpose of this study was to identify methods by which commonly available patient satisfaction indicators could be used to identify potential malpractice litigation risks. Using data from the risk management department of a large academic medical center, we combined yearly administrative records from 1998 to 2006 of malpractice-related litigation activity, with patient satisfaction scores related to attending physicians. We then applied 3 approaches to code patient satisfaction for each year: (1) calculating the overall mean, (2) assigning tertiles, and (3) identifying the minimum satisfaction response to any question. We then estimated 3 versions of random-effect logit models to examine which estimators predicted whether an attending physician was named in a lawsuit in a given year. Minimum satisfaction score was significantly associated with malpractice activity; the other analytic approaches did not yield significant associations. Although patient satisfaction explained little variation in an individual physician's contribution to malpractice risk, accounting for the minimum score explained more than a quarter of a department's contribution. Findings suggest that minimum satisfaction score may provide a useful metric for identifying and prioritizing malpractice risks.