An Alternative Method of Public Reporting of Comparative Hospital Quality and Performance Data for Transparency Initiatives. Issue 9 (13th September 2021)

• Record Type: Journal Article
• Authors: O'Mahen, Patrick; Mehta, Paras; Knox, Melissa K.; Yang, Christine; Kuebeler, Mark; Rajan, Suja S.; Hysong, Sylvia J.; Petersen, Laura A.
• Is Part Of: Medical care. Volume 59:Issue 9(2021)
• Page Start: 816
• Page End: 823
• Publication Date: 2021-09-13
• Subjects: hospital quality -- performance measurement -- quality of care -- quality performance; Economics, Medical -- Periodicals; Insurance, Health -- Periodicals; Santé, Services de -- Administration -- Périodiques; Soins médicaux -- Périodiques; Medical economics -- Periodicals; Health insurance -- Periodicals; Medical economics -- United States -- Periodicals; Health insurance -- United States -- Periodicals; Comprehensive Health Care -- Periodicals; Personal Health Services -- Periodicals; Gezondheidszorg; Économie de la santé -- Périodiques; Santé, Services de -- Périodiques; Health insurance; Medical economics; United States; Periodicals; 362.10973
• DOI: 10.1097/MLR.0000000000001567
• Languages: English
• ISSNs: 0025-7079

Abstract

Abstract : Supplemental Digital Content is available in the text. Abstract : Background: Hospital performance comparisons for transparency initiatives may be inadequate if peer comparison groups are poorly defined. Objective: The objective of this study was to evaluate a new approach identifying hospital peers for comparison. Design/Setting: We used Mahalanobis distance as a new method of developing peer-specific groupings for hospitals to incorporate both external and internal complexity. We compared the overlap in groups with an existing method used by the Veterans' Health Administration's Office for Productivity, Efficiency, and Staffing (OPES). Participants: One hundred twenty-two acute-care Veterans' Health Administration's Medical Facilities as defined in the OPES fiscal year 2014 report. Measures: Using 15 variables in 9 categories developed from expert input, including both hospital internal measures and community-based external measures, we used principal components analysis and calculated Mahalanobis distance between each hospital pair. This method accounts for correlation between variables and allows for variables having different variances. We identified the 50 closest hospitals, then eliminated any potential peer whose score on the first component was >1 SD from the reference hospital. We compared overlap with OPES measures. Results: Of 15 variables, 12 have SDs exceeding 25% of their means. The first 2 components of our analysis explain 24.8% and 18.5% of variation among hospitals. Eight of 9 variables scaling positively on the first component measure internal complexity, aligning with OPES groups. Four of 5 variables scaling positively on the second component but not the first are factors from the policy environment; this component reflects a dimension not considered in OPES groups. Conclusion: Individualized peers that incorporate external complexity generate more nuanced comparators to evaluate quality.