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Health outcomes vary substantially between high and low quality institutions, meaning the difference between life and death in some cases. Prior literature has identified a number of variables that can be used to determine hospital quality, but methodologies for combining variables into an overall measure of hospital quality are not well developed. This analysis builds on the prior investigation of hospital quality by evaluating a method originally developed for the detection of healthcare fraud, Pridit, in the context of determining hospital quality. We developed a theoretical model to justify the application of Pridit to the hospital quality setting and then applied the Pridit method to a national, multiyear dataset on U.S. hospital quality variables and outcomes. The results demonstrate how the Pridit method can be used predictively, in order to predict future health outcomes based on currently available quality measures. These results inform the use of Pridit, and other unsupervised learning methods, in fraud detection and other settings where valid and reliable outcomes variables are difficult to obtain. The empirical results obtained in this study may also be of use to health insurers and policymakers who aim to improve quality in the hospital setting.



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