Background: Patient involvement is widely acknowledged to be a valuable component in health technology assessment (HTA) and healthcare decision making. However, quantitative approaches to ascertain patients' preferences for treatment endpoints are not yet established. The objective of this study is to introduce the analytic hierarchy process
Multi-criteria decision analysis (MCDA) is increasingly used to support decisions in healthcare involving multiple and conflicting criteria. Although uncertainty is usually carefully addressed in health economic evaluations, whether and how the different sources of uncertainty are dealt with and with what methods in MCDA is less known. The objective of this study is to review how uncertainty can be explicitly taken into account in MCDA and to discuss which approach may be appropriate for healthcare decision makers. A literature review was conducted in the Scopus and PubMed databases. Two reviewers independently categorized studies according to research areas, the type of MCDA used, and the approach used to quantify uncertainty. Selected full text articles were read for methodological details. The search strategy identified 569 studies. The five approaches most identified were fuzzy set theory (45 % of studies), probabilistic sensitivity analysis (15 %), deterministic sensitivity analysis (31 %), Bayesian framework (6 %), and grey theory (3 %). A large number of papers considered the analytic hierarchy process in combination with fuzzy set theory (31 %). Only 3 % of studies were published in healthcare-related journals. In conclusion, our review identified five different approaches to take uncertainty into account in MCDA. The deterministic approach is most likely sufficient for most healthcare policy decisions because of its low complexity and straightforward implementation. However, more complex approaches may be needed when multiple sources of uncertainty must be considered simultaneously.Electronic supplementary materialThe online version of this article (doi:10.1007/s40273-014-0251-x) contains supplementary material, which is available to authorized users.
The analytic hierarchy process (AHP) has been increasingly applied as a technique for multi-criteria decision analysis in healthcare. The AHP can aid decision makers in selecting the most valuable technology for patients, while taking into account multiple, and even conflicting, decision criteria. This tutorial illustrates the procedural steps of the AHP in supporting group decision making about new healthcare technology, including (1) identifying the decision goal, decision criteria, and alternative healthcare technologies to compare, (2) structuring the decision criteria, (3) judging the value of the alternative technologies on each decision criterion, (4) judging the importance of the decision criteria, (5) calculating group judgments, (6) analyzing the inconsistency in judgments, (7) calculating the overall value of the technologies, and (8) conducting sensitivity analyses. The AHP is illustrated via a hypothetical example, adapted from an empirical AHP analysis on the benefits and risks of tissue regeneration to repair small cartilage lesions in the knee. Key Points for Decision MakersIn a step-by-step approach, it is illustrated how the analytic hierarchy process (AHP) can support groups making healthcare decisions.The AHP facilitates the decision makers in discussing and valuing the multiple outcomes of alternative healthcare technologies.The AHP can prioritize the healthcare technology to help decision makers in selecting the most valuable technology for patients.
PurposePhotoacoustic (PA) imaging is a recently developed breast cancer imaging technique. In order to enhance successful clinical implementation, we quantified the potential clinical value of different scenarios incorporating PA imaging by means of multi-criteria analysis. From this analysis, the most promising area of application for PA imaging in breast cancer diagnosis is determined, and recommendations are provided to optimize the design of PA imaging.MethodsThe added value of PA imaging was assessed in two areas of application in the diagnostic track. These areas include PA imaging as an alternative to x-ray mammography and ultrasonography in early stage diagnosis, and PA imaging as an alternative to Magnetic Resonance Imaging (MRI) in later stage diagnosis. The added value of PA imaging was assessed with respect to four main criteria (costs, diagnostic performance, patient comfort and risks). An expert panel composed of medical, technical and management experts was asked to assess the relative importance of the criteria in comparing the alternative diagnostic devices. The judgments of the experts were quantified based on the validated pairwise comparison technique of the Analytic Hierarchy Process, a technique for multi-criteria analysis. Sensitivity analysis was applied to account for the uncertainty of the outcomes.ResultsAmong the considered alternatives, PA imaging is the preferred technique due to its non-invasiveness, low cost and low risks. However, the experts do not expect large differences in diagnostic performance. The outcomes suggest that design changes to improve the diagnostic performance of PA imaging should focus on the quality of the reconstruction algorithm, detector sensitivity, detector bandwidth and the number of wavelengths used.ConclusionThe AHP method was useful in recommending the most promising area of application in the diagnostic track for which PA imaging can be implemented, this being early diagnosis, as a substitute for the combined use of x-ray mammography and ultrasonography.
The multi-criteria decision analysis revealed the attributes of the screening techniques that are most important so as to increase intention to participate in a screening program. Even though the respondents may recognize the high importance of diagnostic effectiveness in the long term, their short-term decision to attend the screening tests may be less driven by this consideration. Our analysis suggests that inconvenience, safety, and frequency of the test are the strongest technique-related determinants of the respondents' intention to participate in colorectal screening programs.
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