title: A hospital-specific template for benchmarking its cost and quality.
abstract: OBJECTIVE
  Develop an improved method for auditing hospital cost and quality tailored to a specific hospital's patient population.
  
  
  METHODS
  Medicare claims in general, gynecologic and urologic surgery, and orthopedics from Illinois, New York, and Texas between 2004 and 2006.
  
  
  METHODS
  A template of 300 representative patients from a single index hospital was constructed and used to match 300 patients at 43 hospitals that had a minimum of 500 patients over a 3-year study period.
  
  
  METHODS
  From each of 43 hospitals we chose 300 patients most resembling the template using multivariate matching.
  
  
  RESULTS
  We found close matches on procedures and patient characteristics, far more balanced than would be expected in a randomized trial. There were little to no differences between the index hospital's template and the 43 hospitals on most patient characteristics yet large and significant differences in mortality, failure-to-rescue, and cost.
  
  
  CONCLUSIONS
  Matching can produce fair, directly standardized audits. From the perspective of the index hospital, "hospital-specific" template matching provides the fairness of direct standardization with the specific institutional relevance of indirect standardization. Using this approach, hospitals will be better able to examine their performance, and better determine why they are achieving the results they observe.
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: 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: D000368:Aged; D000369:Aged, 80 and over; D019985:Benchmarking / Q000379:methods*; D005260:Female; D005375:Financial Audit / Q000379:methods*; D017721:Hospital Costs / Q000706:statistics & numerical data*; D006801:Humans; D007087:Illinois; D008297:Male; D006278:Medicare / Q000191:economics* / Q000706:statistics & numerical data*; D008875:Middle Aged; D015233:Models, Statistical; D009518:New York; 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