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Predictive Analytics and Medical Malpractice

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  • Edward W. Frees
  • Lisa Gao

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.

Suggested Citation

  • Edward W. Frees & Lisa Gao, 2020. "Predictive Analytics and Medical Malpractice," North American Actuarial Journal, Taylor & Francis Journals, vol. 24(2), pages 211-227, April.
    • Handle: RePEc:taf:uaajxx:v:24:y:2020:i:2:p:211-227
    DOI: 10.1080/10920277.2019.1634597
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