Method: An expert meeting was held in June 2013 at the University of Sheffield with 29 representatives from a variety of governmental, academic and pharmaceutical institutes, which had the objective to discuss the role, options and limitations of MCDA in health (full details of the participants will be provided). The key messages and good practice recommendations developed by the participants of the expert meeting will be described along with a description of the future work underway.
Result: The key messages and good practice recommendations developed by the participants of the expert meeting are as follows: a) Problem structuring is key: The first aspect of the MCDA process is the problem structuring and it is recommended that enough time is allocated to understand and specify the decision problem under consideration, b) Numerical MCDA modelling is not always necessary: there are different ways to use same information and deliberative discourse with the performance matrix as a starting point is sufficient in some situations rather than numerical MCDA models, c) Variety of weighting and scoring techniques: There are a number of different methods to estimate the value scores and to elicit the weights but not all scoring methods and weighting techniques are suitable for every MCDA method, d) Visualisation/transparency is important: For the decision makers to have confidence in the MCDA model, the model outputs need to be adequately visualised and the model needs to be transparent, e) Uncertainty modelling: appropriate care needs to be taken in performing uncertainty analysis due to the interdependence of uncertainty in evidence and uncertaint in committee members’ preferences.
Conclusion: MCDA has already been used and is well suited to support a broad range of health care decision problems but there is a need to develop a framework to select the appropriate MCDA technique for specific health care decisions. Future work is underway to develop the guidelines for choosing the most appropriate MCDA method to be applied for a given health care decision problem.