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

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

Author information

1
Center for Outcomes Research and Evaluation, Yale-New Haven Health, New Haven, Connecticut, United States of America.
2
Department of Mathematics, Yale University, New Haven, Connecticut, United States of America.
3
Department of Emergency Medicine, Yale School of Medicine, New Haven, Connecticut, United States of America.
4
Department of General Pediatrics, Yale School of Medicine, New Haven, Connecticut, United States of America.
5
Department of Internal Medicine, Section of Cardiovascular Medicine, Yale School of Medicine, New Haven, Connecticut, United States of America.
6
Department of Internal Medicine, Robert Wood Johnson Foundation Clinical Scholars Program, Yale School of Medicine, New Haven, Connecticut, United States of America.
7
Department of Health Policy and Management, Yale School of Public Health, New Haven, Connecticut, United States of America.

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.

PMID:
28662045
PMCID:
PMC5491053
DOI:
10.1371/journal.pone.0179603
[Indexed for MEDLINE]
Free PMC Article

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