Template Matching for Auditing Hospital Cost and Quality
Version of Record online: 3 MAR 2014
© Health Research and Educational Trust
Health Services Research
Volume 49, Issue 5, pages 1446–1474, October 2014
How to Cite
Silber, J. H., Rosenbaum, P. R., Ross, R. N., Ludwig, J. M., Wang, W., Niknam, B. A., Mukherjee, N., Saynisch, P. A., Even-Shoshan, O., Kelz, R. R. and Fleisher, L. A. (2014), Template Matching for Auditing Hospital Cost and Quality. Health Services Research, 49: 1446–1474. doi: 10.1111/1475-6773.12156
- Issue online: 24 SEP 2014
- Version of Record online: 3 MAR 2014
- Agency for Healthcare Research and Quality. Grant Number: R01-HS018338
- National Science Foundation. Grant Number: SBS-10038744
- Quality of care;
- outcomes research;
- health care research;
Develop an improved method for auditing hospital cost and quality.
Medicare claims in general, gynecologic and urologic surgery, and orthopedics from Illinois, Texas, and New York between 2004 and 2006.
A template of 300 representative patients was constructed and then used to match 300 patients at hospitals that had a minimum of 500 patients over a 3-year study period.
Data Collection/Extraction Methods
From each of 217 hospitals we chose 300 patients most resembling the template using multivariate matching.
The matching algorithm found close matches on procedures and patient characteristics, far more balanced than measured covariates would be in a randomized clinical trial. These matched samples displayed little to no differences across hospitals in common patient characteristics yet found large and statistically significant hospital variation in mortality, complications, failure-to-rescue, readmissions, length of stay, ICU days, cost, and surgical procedure length. Similar patients at different hospitals had substantially different outcomes.
The template-matched sample can produce fair, directly standardized audits that evaluate hospitals on patients with similar characteristics, thereby making benchmarking more believable. Through examining matched samples of individual patients, administrators can better detect poor performance at their hospitals and better understand why these problems are occurring.