OBJECTIVES: Reimbursement decisions about innovative medical technologies are notoriously challenging, as they commonly have limited and rapidly evolving evidence, an uncertain position in the care pathway, and may impact criteria beyond clinical-, and cost-effectiveness. Multiple-criteria decision analysis (MCDA) helps evaluate these often conflicting criteria. However, its use in current appraisal processes is scarce and complex. We aimed to develop an adaptive decision tool that facilitates the use of MCDA in the decision-making process. We used the case of broad molecular testing in oncology to illustrate its use.

METHODS: We developed a decision support tool with a web browser-based interface using R Shiny to allow users to easily perform statistical analyses based on their decision-making needs. The tool was developed to include a customizable framework so users can incorporate their data and analyses into the existing tool. An online tutorial is available to showcase the necessary steps. For our application in broad molecular testing, we followed the guidance of the Dutch HealthCare Institute.

RESULTS: The tool allows the entry of MCDA data, uncertainty information, and relative weights for each criterium. It provides visual communication of the MCDA input and results from different perspectives, which can be stored and downloaded. Furthermore, it allows the decision-maker’s assessment of the criteria and ultimate appraisal of the technology. In the case of broad molecular testing, we developed a spiderweb graph to communicate the weighting of five determinants derived from the literature and interviews: feasibility, implications of diagnostics results, laboratories organization, patient journey, and scientific spillover, next to clinical- and cost-effectiveness.

CONCLUSIONS: The R Shiny MCDA decision support tool facilitates systematic and transparent communication of information and preferences from different stakeholders; while fostering evidence-informed decision-making. The systematic approach for including additional determinants besides health benefits and costs is an innovative approach to decision-making in broad molecular testing.