CHOOSING APPROPRIATE MULTI-CRITERIA DECISION ANALYSIS TECHNIQUE(S) TO SUPPORT HEALTH CARE DECISIONS: ISSUES AND CONSIDERATIONS

Purpose: Multi-Criteria Decision Analysis (MCDA) is concerned with decision-making situations in which multiple criteria are explicitly considered, usually resulting in, depending on the type of application, alternatives being ranked (prioritized) or selected or rejected. There has been increase in the number of MCDA applications in healthcare since 1990s but there is still confusion among potential users regarding their appropriate use. There is a need to develop guidelines for choosing the most appropriate MCDA method to be applied for a given health care decision problem.

Method: This paper discusses the main MCDA approaches and methods and provides examples of the diverse range of health care applications in use internationally. The common steps for implementing MCDA are explained, which include 1) carefully structuring the decision problem being addressed; 2) ensuring that appropriate criteria are specified; 3) measuring alternatives’ performance accurately; using valid and reliable methods for 4) scoring alternatives and 5) weighting criteria; and 6) presenting MCDA results, including 7) sensitivity analysis, in a form that is relatively easily interpreted and communicated. However, the way these steps are conducted differentiate the MCDA methods.

Result: Most applications of MCDA in health care are based on weighted-sum models. Notwithstanding the popularity of this approach, methodological issues arise at each step of the process for creating and applying such models. In particular, there are a potentially confusing variety of scoring and weighting methods (steps 4 and 5) to choose from. Naturally, all methods (and software implementing them) have their relative strengths and weaknesses (choosing the ‘best’ MCDA method is itself a multi-criteria decision problem!).

When thinking about which scoring and weighting methods to use consideration needs to be given to: how well methods elicit trade-offs between criteria; the time and resources required to implement alternative methods; the cognitive burden imposed on participants and whether skilled facilitators are required; the need for additional data processing and statistical analysis; the validity of the underlying assumptions relative to decision-makers’ preferences; and whether the outputs produced will satisfy decision-makers’ objectives.

Conclusion: As the use of MCDA in health care increases, further research into the development of a framework to help select the most appropriate methods for particular types of health care application would be worthwhile.