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Template Matching for Auditing Hospital Cost and Quality
Author(s) -
Silber Jeffrey H.,
Rosenbaum Paul R.,
Ross Richard N.,
Ludwig Justin M.,
Wang Wei,
Niknam Bijan A.,
Mukherjee Nabanita,
Saynisch Philip A.,
EvenShoshan Orit,
Kelz Rachel R.,
Fleisher Lee A.
Publication year - 2014
Publication title -
health services research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.706
H-Index - 121
eISSN - 1475-6773
pISSN - 0017-9124
DOI - 10.1111/1475-6773.12156
Subject(s) - benchmarking , medicine , audit , matching (statistics) , data extraction , propensity score matching , sample (material) , data collection , emergency medicine , case mix index , medline , statistics , surgery , nursing , accounting , chemistry , pathology , marketing , chromatography , business , mathematics , political science , law
Objective Develop an improved method for auditing hospital cost and quality. Data Sources/Setting Medicare claims in general, gynecologic and urologic surgery, and orthopedics from I llinois, T exas, and N ew Y ork between 2004 and 2006. Study Design 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. Principal Findings 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. Conclusion 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.