title: Template matching for auditing hospital cost and quality.
abstract: OBJECTIVE
  Develop an improved method for auditing hospital cost and quality.
  
  
  METHODS
  Medicare claims in general, gynecologic and urologic surgery, and orthopedics from Illinois, Texas, and New York between 2004 and 2006.
  
  
  METHODS
  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.
  
  
  METHODS
  From each of 217 hospitals we chose 300 patients most resembling the template using multivariate matching.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: The Department of Pediatrics, The University of Pennsylvania School of Medicine, Philadelphia, PA; Department of Anesthesiology and Critical Care, The University of Pennsylvania School of Medicine, Philadelphia, PA; Department of Health Care Management, The Wharton School, The University of Pennsylvania, Philadelphia, PA; The Leonard Davis Institute of Health Economics, The University of Pennsylvania, Philadelphia, PA.
authors.forename: Jeffrey H
authors.initials: JH
authors.lastname: Silber
authors.forename: Paul R
authors.initials: PR
authors.lastname: Rosenbaum
authors.forename: Richard N
authors.initials: RN
authors.lastname: Ross
authors.forename: Justin M
authors.initials: JM
authors.lastname: Ludwig
authors.forename: Wei
authors.initials: W
authors.lastname: Wang
authors.forename: Bijan A
authors.initials: BA
authors.lastname: Niknam
authors.forename: Nabanita
authors.initials: N
authors.lastname: Mukherjee
authors.forename: Philip A
authors.initials: PA
authors.lastname: Saynisch
authors.forename: Orit
authors.initials: O
authors.lastname: Even-Shoshan
authors.forename: Rachel R
authors.initials: RR
authors.lastname: Kelz
authors.forename: Lee A
authors.initials: LA
authors.lastname: Fleisher
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D019985:Benchmarking / Q000379:methods*; D054869:Clinical Audit / Q000706:statistics & numerical data*; D005260:Female; D013502:General Surgery / Q000706:statistics & numerical data; D017721:Hospital Costs / Q000706:statistics & numerical data*; D061847:Hospitals, High-Volume / Q000706:statistics & numerical data; D061846:Hospitals, Low-Volume / Q000706:statistics & numerical data; D006801:Humans; D007087:Illinois; D008297:Male; D006278:Medicare / Q000706:statistics & numerical data*; D008875:Middle Aged; D015233:Models, Statistical; D009518:New York; D009985:Orthopedics / Q000706:statistics & numerical data; D017063:Outcome Assessment, Health Care / Q000706:statistics & numerical data*; D011787:Quality of Health Care / Q000706:statistics & numerical data*; D013781:Texas; D014481:United States
keywords: Quality of care; cost; health care research; outcomes research