Describing the performance of U.S. hospitals by applying big data analytics

PLoS One. 2017 Jun 29;12(6):e0179603. doi: 10.1371/journal.pone.0179603. eCollection 2017.

Abstract

Public reporting of measures of hospital performance is an important component of quality improvement efforts in many countries. However, it can be challenging to provide an overall characterization of hospital performance because there are many measures of quality. In the United States, the Centers for Medicare and Medicaid Services reports over 100 measures that describe various domains of hospital quality, such as outcomes, the patient experience and whether established processes of care are followed. Although individual quality measures provide important insight, it is challenging to understand hospital performance as characterized by multiple quality measures. Accordingly, we developed a novel approach for characterizing hospital performance that highlights the similarities and differences between hospitals and identifies common patterns of hospital performance. Specifically, we built a semi-supervised machine learning algorithm and applied it to the publicly-available quality measures for 1,614 U.S. hospitals to graphically and quantitatively characterize hospital performance. In the resulting visualization, the varying density of hospitals demonstrates that there are key clusters of hospitals that share specific performance profiles, while there are other performance profiles that are rare. Several popular hospital rating systems aggregate some of the quality measures included in our study to produce a composite score; however, hospitals that were top-ranked by such systems were scattered across our visualization, indicating that these top-ranked hospitals actually excel in many different ways. Our application of a novel graph analytics method to data describing U.S. hospitals revealed nuanced differences in performance that are obscured in existing hospital rating systems.

MeSH terms

  • Centers for Medicare and Medicaid Services, U.S.
  • Hospital Administration*
  • United States

Grants and funding

The authors received no specific funding for this work. Angela Hsieh was a full-time employee of the Center for Outcomes Research and Evaluation at Yale University when this work was conducted, and accepted a position at Genentech as the initial draft of this manuscript was being written. This does not alter our adherence to PLOS ONE policies on sharing data and materials. Genentech had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript, nor did it provide any form of financial support for any of the investigators, with the exception of the employment of Angela Hsieh outside of the time she spent on this manuscript. Alexander Cloninger was partially supported by the National Science Foundation grant DMS-1402254.