Predictive Analytics and Medical Malpractice

Abstract

Insurance as a discipline has long embraced analytics, and market trends signal an even stronger relationship going forward. This article is a case study on the use of predictive analytics in the context of medical errors. Analyzing medical errors helps improve health care systems, and through a type of insurance known as medical malpractice insurance, we have the ability to analyze medical errors using data external to the health care system. In the spirit of modern analytics, this paper describes the application of data from several different sources. These sources give different insights into a specific problem facing the medical malpractice community familiar to actuaries: the relative importance of upper limits (or caps) on insurance payouts for noneconomic damages (e.g., pain and suffering). This topic is important to the industry in that many courts are considering the legality of such limitations. All stakeholders, including patients, physicians, hospitals, lawyers, and the general public, are interested in the implications of removing limitations on caps. This article demonstrates how we can use data and analytics to inform the many different stakeholders on this issue.

Acknowledgments

We acknowledge a Society of Actuaries CAE Grant for support of this work. The first author acknowledges support from the University of Wisconsin-Madison’s Hickman-Larson Chair in Actuarial Science. The second author acknowledges support from the Society of Actuaries James C. Hickman Scholar program.

SUPPLEMENTAL MATERIAL

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