Cost-effectiveness modelling in diagnostic imaging: a stepwise approach

Eur Radiol (2015) 25:3629–3637 DOI 10.1007/s00330-015-3770-8 HEALTH ECONOMY Cost-effectiveness modelling in diagnostic imaging: a stepwise approach Anna M. Sailer 1,2 & Wim H. van Zwam 1 & Joachim E. Wildberger 1 & Janneke P. C. Grutters 3 Received: 6 March 2015 / Accepted: 3 April 2015 / Published online: 24 May 2015 # The Author(s) 2015. This article is published with open access at Springerlink.com Abstract Diagnostic imaging (DI) is the fastest growing sector in medical expenditures and takes a central role in medical decisionmaking. The increasing number of various and new imaging technologies induces a growing demand for cost-effectiveness analysis (CEA) in imaging technology assessment. In this article we provide a comprehensive framework of direct and indirect effects that should be considered for CEA in DI, suitable for all imaging modalities. We describe and explain the methodology of decision analytic modelling in six steps aiming to transfer theory of CEA to clinical research by demonstrating key principles of CEA in a practical approach. We thereby provide radiologists with an introduction to the tools necessary to perform and interpret CEA as part of their research and clinical practice. Key Points • DI influences medical decision making, affecting both costs and health outcome. • This article provides a comprehensive framework for CEA in DI. • A six-step methodology for conducting and interpreting costeffectiveness modelling is proposed. * Anna M. Sailer 1 Department of Radiology, Maastricht University Medical Center, P.O. Box 5800, P.Debyelaan 25, Maastricht 6202 AZ, The Netherlands 2 Department of Radiology, Stanford University Hospitals and Clinics, Stanford, CA, USA 3 Department for Health Evidence, Radboud University Medical Center, P.O. Box 9101, Geert Grooteplein-Zuid 10, Nijmegen 6500 HB, The Netherlands Keywords Cost Effectiveness . Decision Modelling . Diagnostic Imaging . Economics . Technology Assessment Introduction Increasing scrutiny of healthcare costs leads to a demand for proof of value for all medical expenditures. Cost-effectiveness analyses (CEA) intend to provide additional information about the possibilities of maximizing health effects, taking into account limited health care resources [1]. CEA have already become common practice in the evaluation of disease treatment strategies and diagnostic screening programs [2]. Diagnostic imaging (DI) is currently the fastest growing category in medical expenditure [3, 4]. Over the last years, an increasing number of CEA of DI technologies have been published [5–12], though broad application has yet to happen. The distinct central role diagnostic imaging plays in medical decision-making, as well as the continued emergence of new and varied imaging technologies, increases the importance of costeffectiveness evaluation in imaging technology assessment. Several articles provide an overview on the theory of CEA in DI [13–15]. Although they contain excellent technical background, radiologists and other DI professionals still might feel insecure in performing and interpreting CEA, as economic evaluation is not part of medical training. Even for those doctors who received additional training, performing CEA analyses in DI is challenging due to missing standardized methodologies. Furthermore, the effects both on costs and health outcome largely depend on the treatment strategy decisions that are made based on the imaging results themselves. Consideration of these remote indirect effects requires more complex methodologies for CEA in DI compared to CEA in therapeutic services [16]. Synthesis of available evidence incorporated in decision analytic modelling forms the link 3630 between a diagnostic test and its effects in terms of costs and health outcome. A comprehensive practical guide to the use of decision modelling techniques can be found in the book of Briggs et al. [17]. The aim of this article is to provide an introduction to the tools necessary to perform and interpret CEA. We thereby transfer the theory of evidence synthesis and decision analytic modelling to practical clinical research by demonstrating key principles and steps of CEA in diagnostic imaging. Rationale of cost-effectiveness analysis in diagnostic imaging A cost-effectiveness analysis is the comparative analysis of alternative courses of action in terms of both their costs and consequences. In imaging, these alternative courses of action can be utilization of different imaging techniques, or, more generally, imaging versus no imaging. The rationale of CEA in DI is that the choice of DI test influences both costs as well as effectiveness of disease management. In a conceptual framework developed by Fineberg et al. [18] and modified by others [19], effectiveness of a diagnostic test is expressed on subsequent hierarchical levels: technical performance, diagnostic accuracy, diagnostic impact, therapeutic impact and health outcome. Effectiveness in terms of patients’ health outcome is indirectly influenced by the diagnostic test due to medical care decisions based on imaging. The health outcome can also directly be affected by the imaging test itself. Health effects can be physical, for example because of altered treatment, and psychological, for example because of receiving a diagnosis. Direct and indirect health effects can be measured in utility scores, from which Quality Adjusted Life Years (QALYs) can be derived, combining survival and quality of life. Both physical and psychological health conditions are incorporated in a QALY. Looking at costs, these are directly affected by the costs of the diagnostic test itself as well as indirectly influenced by costs of treatment chosen based on imaging and resulting costs of patients’ health outcome. Figure 1 illustrates the concept of these direct and indirect effects used in CEA in DI. When assessing the cost-effectiveness of DI, the initial question is whether adding an imaging test in a medical pathway does improve medical decision-making. Taking a hypothetical 100 % accuracy for a test would be used to assess changes in outcome by adding this particular test to the medical pathway. Only if the perfectly accurate test provides added value, it is reasonable to continue the analysis with the actual sensitivity and specificity of the test [20]. However, in many clinical situations imaging is already an existing standard part of the particular disease management. CEA is, therefore, used to compare potential new imaging technologies or Eur Radiol (2015) 25:3629–3637 imaging strategies to each other as well as to the current reference standard. We will focus on this second model throughout the article. Decision-analytic modelling While studies are being performed to inform decision makers about optimal DI tests, these studies often focus on the accuracy and short-term effects of the imaging test. However, for decision making it is also important to know how well a test (...truncated)