Evaluating the effects of variation in clinical practice: a risk adjusted cost-effectiveness (RAC-E) analysis of acute stroke services

BackgroundMethods for the cost-effectiveness analysis of health technologies are now well established, but such methods may also have a useful role in the context of evaluating the effects of variation in applied clinical practice. This study illustrates a general methodology for the comparative analysis of applied clinical practice at alternative institutions – risk adjusted cost-effectiveness (RAC-E) analysis – with an application that compares acute hospital services for stroke patients admitted to the main public hospitals in South Australia.MethodsUsing linked, routinely collected data on all South Australian hospital separations from July 2001 to June 2008, an analysis of the RAC-E of services provided at four metropolitan hospitals was undertaken using a decision analytic framework. Observed (plus extrapolated) and expected lifetime costs and survival were compared across patient populations, from which the relative cost-effectiveness of services provided at the different hospitals was estimated.ResultsUnadjusted results showed that at one hospital patients incurred fewer costs and gained more life years than at the other hospitals (i.e. it was the dominant hospital). After risk adjustment, the cost minimizing hospital incurred the lowest costs, but with fewer life-years gained than one other hospital. The mean incremental cost per life-year gained of services provided at the most effective hospital was under $20,000, with an associated 65% probability of being cost-effective at a $50,000 per life year monetary threshold.ConclusionsRAC-E analyses can be used to identify important variation in the costs and outcomes associated with clinical practice at alternative institutions. Such data provides an impetus for further investigation to identify specific areas of variation, which may then inform the dissemination of best practice service delivery and organisation.

[1]  Robert Pampalon,et al.  A comparison of individual and area-based socio-economic data for monitoring social inequalities in health. , 2009, Health reports.

[2]  J. Hutton,et al.  A Comparison of the Costs and Survival of Hospital-Admitted Stroke Patients Across Europe , 2001, Stroke.

[3]  Melissa L Dougherty,et al.  Redesigning care at the Flinders Medical Centre: clinical process redesign using “lean thinking” , 2008, The Medical journal of Australia.

[4]  A. Brennan,et al.  Modelling the long term cost effectiveness of clopidogrel for the secondary prevention of occlusive vascular events in the UK , 2005, Current medical research and opinion.

[5]  Paul C. Lambert,et al.  Further Development of Flexible Parametric Models for Survival Analysis , 2009 .

[6]  Wil M. P. van der Aalst,et al.  Application of Process Mining in Healthcare - A Case Study in a Dutch Hospital , 2008, BIOSTEC.

[7]  P. Royston,et al.  Flexible parametric proportional‐hazards and proportional‐odds models for censored survival data, with application to prognostic modelling and estimation of treatment effects , 2002, Statistics in medicine.

[8]  Melissa L Dougherty,et al.  Patient journeys: the process of clinical redesign , 2008, The Medical journal of Australia.

[9]  Tammy Hoffmann,et al.  Clinical Guidelines for Stroke Management 2010 , 2010 .

[10]  A Wajda,et al.  The art and science of record linkage: methods that work with few identifiers. , 1986, Computers in biology and medicine.

[11]  C. Holman,et al.  Improved methods for estimating incidence from linked hospital morbidity data. , 2003, International journal of epidemiology.

[12]  A Wajda,et al.  Record Linkage Strategies , 1991, Methods of Information in Medicine.

[13]  Jonathan Karnon,et al.  Applying risk adjusted cost-effectiveness (RAC-E) analysis to hospitals: estimating the costs and consequences of variation in clinical practice. , 2013, Health economics.

[14]  N Freemantle,et al.  Social deprivation and prognostic benefits of cardiac surgery: observational study of 44 902 patients from five hospitals over 10 years , 2009, BMJ : British Medical Journal.