Using Data Analytics to Improve Hospital Quality Performance.

The objective of this study was to build a unified quality performance model for hospitals using publicly available data. We obtained data from the New York State Department of Health's Statewide Planning and Research Cooperative System database for our model, which had three outcome measures that we wished to make smaller (deaths, readmissions, average length of stay). Because this was a performance model rather than an economic efficiency model, we excluded costs, which are affected significantly by local economic conditions. We included four site characteristics. With our data envelopment analysis model structure, we used logistic regression to analyze the output. We extracted data for 2,233,214 discharges in 2014 from 183 hospitals in the state. We found that 20.8% of the facilities were on the quality performance frontier-20.6% of the not-for-profit facilities and 21.4% of the other facilities. Hospitals with more discharges performed better with respect to mortality, readmission, and average length of stay. We found no difference in performance between not-for-profit hospitals and others. We concluded that 79.2% of hospitals could improve their quality of care. As an upper bound, if all hospitals increased each quality factor performance to 100%, there would have been 11,722 (24.8%) fewer deaths, 17,840 (15.8%) fewer readmissions, and the statewide average length of stay would have been 0.71 days (13.5%) less.