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APHA Scientific Session and Event Listing

Recorded presentation

Jacob Jen-Hao Cheng, PhD, MS¹, Carlos Alzola, MS², Nikolas Matthes, MD, PhD, MPH³, Samuel Ogunbo, PhD¹, and Devayani Sinha, MS¹. (1) Research & Development, Quality Indicator Project / Maryland Hospital Association, 6816 Deerpath Rd, Elkridge, MD 21075, (410)796-6250, jcheng@mhaonline.org, (2) Data Insights, 401 SE Glyndon St., Vienna, VA 22180, (3) Bloomberg School of Public Health, Johns Hopkins University, 615 N. Wolfe Street, Baltimore, MD 21205

As part of CMS' 8th Statement-of-Work, the Hospital Quality Alliance's ten-measure “starter set” and JCAHO's Oryx initiative, hospitals must submit data on publicly-reported National Hospital Quality Measures (NHQM). Against this backdrop, CMS developed its Composite Quality Score (CQS) methodology to identify high-performing hospitals and to reward superior performance with higher reimbursement. The CQS incorporates performance across multiple measures, thereby offering a broader view of performance. Growing interest in Pay-for-Performance and use of NHQM methodology by several Pay-for-Performance pilots encourages additional analysis of high-performers in order to determine if certain factors contribute to hospitals' ability to sustain superior performance.

This study evaluates hospital performance for heart failure measures over time to identify hospitals that consistently maintain a high level of performance. In addition, the study identifies characteristics common to consistently high-performing hospitals.

Hospital performance data for 475 acute care hospitals using the NHQM heart failure measures through the Quality Indicator Project® during 2005 were combined with a characteristics database for those hospitals. The combined dataset conveys 4-by-12 (measures by months) performance data and 39 characteristic variables. To provide a broader picture, moving average was applied to filter the longitudinal data of composite scores. “Best hospitals” were defined as those consistently above the 80th percentile.

Logistic regression was applied to pinpoint hospital characteristics related to high performance, including region, size, type, and participation in multiple measures. Specific hospital characteristics are associated with consistently high performance for heart failure and can be used as a guideline for quality improvement.

Learning Objectives:

• Evaluate hospital performance with a broader picture by dealing with the longitudinal data across multiple measures.
• Identify general hospital characteristics which are associated with consistently high performance for heart failure measures.
• Discuss how to interprete the identified factors as a guideline for quality improvement based on the feedback from performance measurements.

Keywords: Performance Measures, Health Care Quality

Presenting author's disclosure statement:

Any relevant financial relationships? No

Recorded presentation