Evaluation of a clinical decision support tool for matching cancer patients to clinical trials
There is a growing need for alternative methodologies to evaluate digital health solutions in a short timeframe and at relatively low cost. Simulation-based research (SBR) methods have been proposed as an alternative methodology for evaluating digital health solutions; however, few studies have described the applicability of SBR methods to evaluate such solutions. This study used SBR to evaluate the feasibility and user experience of a clinical decision support (CDS) tool used for matching cancer patients to clinical trials. Twenty-five clinicians and research staff were recruited to match 10 synthetic patient cases to clinical trials using both the CDS tool and publicly available online trial databases. Participants were significantly more likely to report having sufficient time (p = 0.020) and to require less mental effort (p = 0.001) to complete trial matching with the CDS tool. Participants required less time for trial matching using the CDS tool, but the difference was not significant (p = 0.093). Most participants reported that they had sufficient guidance to participate in the simulations (96%). This study demonstrates the use of SBR methods is a feasible approach to evaluate digital health solutions and to collect valuable user feedback without the need for implementation in clinical practice. Further research is required to demonstrate the feasibility of using SBR to conduct remote evaluations of digital health solutions.
Digital health, simulation-based research, evidence generation, clinical decision support tools
Evaluation of a clinical decision support tool for matching cancer patients to clinical trials using simulation-based research - Clarissa Gardner, Jack Halligan, Gianluca Fontana, Roberto Fernandez Crespo, Matthew Prime, Chaohui Guo, Okan Ekinci, Saira Ghafur, Ara Darzi, 2022 (sagepub.com)