Provider profiling and performance-based incentive programs have expanded in recent years but need a theoretical framework for measuring and comparing the ``value'' of clinical care across medical providers. Cost-effectiveness analysis provides such a framework but has rarely been used outside of the treatment choice context. The authors present a profiling framework based on cost-effectiveness methods and illustrate their approach using data on in-hospital survival and the cost of care for a heart attack from a sample of Massachusetts hospitals during fiscal year 2003. They model each outcome using hierarchical models that allow performance to vary across hospitals as a function of a latent quality effect and an effect of case mix. They also estimate incremental outcomes by conditioning on each hospital's pair of random effects, using indirect standardization to estimate ``expected'' outcomes, and then taking their difference. Incremental cost and effectiveness outcomes are combined using incremental net monetary benefits. Using cost-effectiveness methods to profile hospital ``value'' permits the comparison of the benefit of a service relative to the cost using existing societal weights.
Herzlinger RE , Parsa-Parsi R.Consumer-driven health care: lessons from Switzerland . JAMA. 2004;292:1213-20.
2.
Lo Sasso AT, Rice T., Gabel JR, Whitmore H.Tales from the new frontier: pioneers' experiences with consumer-driven health care. Health Serv Res. 2004;39:1071-90.
3.
Fuhrmans V.A big insurer bets on hot trend: shopping around for health care. Wall Street Journal.2005 Oct 24;A1.
4.
Robinson JCHospital tiers in health insurance: balancing consumer choice with financial incentives. Health Aff (Millwood). 2003 ;Suppl Web Exclusives:W3-135-46.
5.
Leapfrog Hospital Rewards Program [homepage on the Inter-net]. The Leapfrog Group. [updated 2006 Jan 31; cited 2006 Feb 2]. Available from: https://leapfrog.medstat.com/hrp/index.asp
6.
Hospital Quality Incentive Demonstration [homepage on Inter-net]. Premier, Inc. [updated 2006; cited 2006 Feb 2]. Available from: http://www.premierinc.com/all/quality/hqi/
7.
Medicare Physician Group Practice Demonstration fact sheet.Office of Research, Development, and Information, Centers for Medicare and Medicaid Services, Department of Health & Human Services . [updated 2005 Jan 31; cited 2007 Jan 10]. Available from: http://www.cms.hhs.gov/DemoProjectsEvalRpts/downloads/PGP_Fact_Sheet.pdf
8.
Care Management for High-Cost Beneficiaries demonstration press release . Office of External Affairs, Centers for Medicare and Medicaid Services, Department of Health & Human Services. [updated 2004 Oct 1; cited 2007 Jan 10. Available from: http://www.cms.hhs.gov/DemoProjectsEvalRpts/downloads/CMHCB_press_release_10_2004.pdf
9.
Krumholz HM, Wang Y., Mattera JA, et al. An administrative claims model suitable for profiling hospital performance based on 30-day mortality rates among patients with an acute myocardial infarction. Circulation. 2006;113:1683-92.
10.
Fisher ES, Wennberg DE, Stukel TA, Gottlieb DJVariations in the longitudinal efficiency of academic medical centers . Health Aff (Millwood). 2004;Suppl Web Exclusive:VAR19-32.
11.
Draper D., Gittoes M.Statistical analysis of performance indicators in UK higher education . J R Stat Soc Ser A. 2004;167:449-74.
12.
Normand SL, Glickman ME, Gatsonis CAStatistical methods for profiling providers of medical care: issues and applications . J Am Stat Assoc. 1997;92:803-14.
13.
Burgess JF Jr, Christiansen CL, Michalak SE, Morris CNMedical profiling: improving standards and risk adjustments using hierarchical models. J Health Econ . 2000;19:291-309.
14.
Krumholz HM , Brindis RG, Brush JE, et al. Standards for statistical models used for public reporting of health outcomes: an American Heart Association scientific statement from the Quality of Care and Outcomes Research Interdisciplinary Writing Group. Circulation. 2006;113:456-62.
15.
Grieve R., Nixon R., Thompson SG, Normand C.Using multilevel models for assessing the variability of multinational resource use and cost data. Health Econ. 2005 ;14:185-96.
16.
Thompson SG , Nixon RM, Grieve R.Addressing the issues that arise in analysing multicentre cost data, with application to a multinational study. J Health Econ. 2006;25:1015-28.
17.
Nixon RM, Thompson SGMethods for incorporating covariate adjustment, subgroup analysis and between-centre differences into cost-effectiveness evaluations. Health Econ . 2005;14:1217-29.
18.
O'Hagan A., Stevens JW, Montmartin J.Bayesian cost-effectiveness analysis from clinical trial data. Stat Med. 2001;20:733-53.
19.
Willan AR, Briggs AH, Hoch JSRegression methods for covariate adjustment and subgroup analysis for non-censored cost-effectiveness data. Health Econ. 2004 ;13:461-75.
20.
Willan AR, Pinto EM, O'Brien BJ, et al. Country specific cost comparisons from multinational clinical trials using empirical Bayesian shrinkage estimation: the Canadian ASSENT-3 economic analysis. Health Econ. 2005;14:327-38.
21.
Spiegelhalter DJ, Abrams KR, Myles JPBayesian Approaches to Clinical Trials and Health-Care Evaluation. Chichester , UK: John Wiley;2004.
22.
Stinnett AA , Mullahy J.Net health benefits: a new framework for the analysis of uncertainty in cost-effectiveness analysis. Med Decis Making. 1998;18:S68-80.
23.
Viscusi W., Aldy J.The Value of a Statistical Life: A Critical Review of Market Estimates throughout the World. Cambridge, UK: NBER Working Paper Series; 2003.
24.
Cutler DMYour Money orYour Life: Strong Medicine for America's Health Care System. New York: Oxford University Press; 2004.
25.
Goldstein H. , Speigelhalter D.League tables and their limitation: statistical issues in comparisons of institutional performance (with discussion) . J R Stat Soc Ser A. 1996;159:385-443.
26.
Hannan EL, Kilburn H., O'Donnell JF, Lukacik G., Shields EPAdult open heart surgery in New York state: an analysis of risk factors and hospital mortality rates. JAMA. 1990;264:2768-74.
27.
O'Connor GT , Plume SK, Olmstead EM, et al. Multivariate prediction of in-hospital mortality associated with coronary artery bypass graft surgery. Northern New England Cardiovascular Disease Study Group. Circulation. 1992;85:2110-8.
28.
Krumholz HM , Wang Y., Mattera JA, et al. An administrative claims model suitable for profiling hospital performance based on 30-day mortality rates among patients with heart failure. Circulation. 2006;113:1693-701.
29.
Table 1a. National adjusted operating standardized amounts, labor/nonlabor. Federal Register. 2002;67:50134.
30.
O'Hagan A., Stevens JWA framework for cost-effectiveness analysis from clinical trial data. Health Econ. 2001 ;10:303-15.
31.
Duan N.Smearing estimate: a non-parametric retransformation method. J Am Stat Assoc. 1983;78:605-10.
32.
Lunn DJ, Thomas A., Best N., Spiegelhalter D.WinBUGS-a Bayesian modelling framework: concepts, structure, and extensibility . Statist Comput. 2000;10:325-37.
33.
Brooks SP, Gelman A.Alternative methods for monitoring convergence of iterative simulations . J Comp Graph Stat. 1998;7:434-55.
34.
Lothgren M. , Zethraeus N.Definition, interpretation and calculation of cost-effectiveness acceptability curves. Health Econ. 2000;9:623-30.
35.
Fenwick E., Claxton K., Sculpher M.Representing uncertainty: The role of cost-effectiveness acceptability curves . Health Econ. 2001;10:779-87.
36.
Fenwick E., O'Brien BJ, Briggs A.Cost-effectiveness acceptability curves-facts, fallacies and frequently asked questions. Health Econ. 2004;13:405-15.
37.
Committee on Redesigning Health Insurance Performance Measures, Payment, and Performance Improvement Programs.Performance Measurement: Accelerating Improvement. Washington, DC: National Academies Press; 2006.
