A hospital-specific template for benchmarking its cost and quality.

OBJECTIVE Develop an improved method for auditing hospital cost and quality tailored to a specific hospital's patient population. DATA SOURCES/SETTING Medicare claims in general, gynecologic and urologic surgery, and orthopedics from Illinois, New York, and Texas between 2004 and 2006. STUDY DESIGN 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. DATA COLLECTION/EXTRACTION METHODS From each of 43 hospitals we chose 300 patients most resembling the template using multivariate matching. PRINCIPAL FINDINGS 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. CONCLUSION 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.

[1]  Wei Wang,et al.  Template matching for auditing hospital cost and quality. , 2014, Health services research.

[2]  P. Rosenbaum,et al.  Racial Disparities in Operative Procedure Time: The Influence of Obesity , 2013, Anesthesiology.

[3]  Magdalena Cerdá,et al.  Effect of the 2010 Chilean Earthquake on Posttraumatic Stress: Reducing Sensitivity to Unmeasured Bias Through Study Design , 2013, Epidemiology.

[4]  J. Zubizarreta Journal of the American Statistical Association Using Mixed Integer Programming for Matching in an Observational Study of Kidney Failure after Surgery Using Mixed Integer Programming for Matching in an Observational Study of Kidney Failure after Surgery , 2022 .

[5]  P. Rosenbaum,et al.  Medical and Financial Risks Associated With Surgery in the Elderly Obese , 2012, Annals of surgery.

[6]  Dylan S Small,et al.  Optimal Matching with Minimal Deviation from Fine Balance in a Study of Obesity and Surgical Outcomes , 2012, Biometrics.

[7]  P. Rosenbaum,et al.  Estimating Anesthesia Time Using the Medicare Claim: A Validation Study , 2011, Anesthesiology.

[8]  Dylan S. Small,et al.  Using the Cross-Match Test to Appraise Covariate Balance in Matched Pairs , 2010 .

[9]  P. Rosenbaum,et al.  The Hospital Compare mortality model and the volume-outcome relationship. , 2010, Health services research.

[10]  Dylan S. Small,et al.  Sensitivity Analysis for the Cross-Match Test, With Applications in Genomics , 2010 .

[11]  P. Rosenbaum,et al.  Amplification of Sensitivity Analysis in Matched Observational Studies , 2009, Journal of the American Statistical Association.

[12]  T. Urech,et al.  Method to develop health care peer groups for quality and financial comparisons across hospitals. , 2009, Health services research.

[13]  D. Rubin For objective causal inference, design trumps analysis , 2008, 0811.1640.

[14]  Patrick S. Romano,et al.  Failure-to-Rescue: Comparing Definitions to Measure Quality of Care , 2007, Medical care.

[15]  Jeffrey H Silber,et al.  Does ovarian cancer treatment and survival differ by the specialty providing chemotherapy? , 2007, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[16]  P. Rosenbaum,et al.  Minimum Distance Matched Sampling With Fine Balance in an Observational Study of Treatment for Ovarian Cancer , 2007 .

[17]  P. Rosenbaum,et al.  Influence of Patient and Hospital Characteristics on Anesthesia Time in Medicare Patients Undergoing General and Orthopedic Surgery , 2007, Anesthesiology.

[18]  Paul R. Rosenbaum,et al.  Estimating Anesthesia and Surgical Procedure Times from Medicare Anesthesia Claims , 2007, Anesthesiology.

[19]  D. Rubin The design versus the analysis of observational studies for causal effects: parallels with the design of randomized trials , 2007, Statistics in medicine.

[20]  S. Normand,et al.  An Administrative Claims Model Suitable for Profiling Hospital Performance Based on 30-Day Mortality Rates Among Patients With an Acute Myocardial Infarction , 2006, Circulation.

[21]  P. Rosenbaum An exact distribution‐free test comparing two multivariate distributions based on adjacency , 2005 .

[22]  Wei Chen,et al.  Preoperative Antibiotics and Mortality in the Elderly , 2005, Annals of surgery.

[23]  P. J. Huber Robust Statistics: Huber/Robust Statistics , 2005 .

[24]  J. Fleiss,et al.  The Standardization of Rates , 2004 .

[25]  Sankey V. Williams,et al.  Hospital and Patient Characteristics Associated With Death After Surgery: A Study of Adverse Occurrence and Failure to Rescue , 1992, Medical care.

[26]  P. Rosenbaum Sensitivity analysis for matching with multiple controls , 1988 .

[27]  D. Wolfe,et al.  Nonparametric Statistical Methods. , 1974 .

[28]  O S Miettinen,et al.  Individual matching with multiple controls in the case of all-or-none responses. , 1969, Biometrics.

[29]  W. Kruskal,et al.  Use of Ranks in One-Criterion Variance Analysis , 1952 .

[30]  K. Lohr,et al.  Differences among hospitals in Medicare patient mortality. , 1989, Health services research.