Performance Measurement Outcomes: An Analysis of Health Care-Associated Infections in New York State

Area of Science:

• Health Services Research
• Biostatistics
• Infectious Disease Epidemiology

Background:

• Comparing healthcare outcomes, such as hospital infection rates, is crucial for quality improvement.
• Existing New York State (NYS) methodologies for reporting hospital infection rates have limitations.
• Accurate and fair comparison of hospital performance is essential for effective healthcare management.

Purpose of the Study:

• To develop and present a novel methodology for comparing hospital infection rates that overcomes limitations of current approaches.
• To provide a more comprehensive and easily interpretable method for analyzing hospital performance.
• To identify consistently high-performing and low-performing hospitals for targeted interventions.

Main Methods:

• A replication study using New York State data.
• Application of binomial and Poisson distributions to calculate upper tail probabilities (UTP) for poor performers and lower tail probabilities (LTP) for good performers.
• Analysis of hospital performance over time.

Main Results:

• The tail probability method yields results comparable to the NYS confidence interval approach across all healthcare-associated infection (HAI) categories.
• This new methodology allows for the analysis of any hospital, irrespective of patient volume or procedure numbers.
• Consistent poor and good performers were identified, enabling longitudinal performance evaluation.

Conclusions:

• The tail probability method offers a more comprehensive and interpretable approach to analyzing hospital infection rates compared to existing NYS methods.
• Identifying consistently performing hospitals allows for focused resource allocation and quality improvement efforts.
• This approach can aid healthcare administrators and clinicians in addressing infection rates and establishing performance benchmarks.