Selected article for: "clinical decision support tool and decision support"

Author: Gardner, C. J.; Halligan, J.; Fontana, G.; Fernandez Crespo, R.; Prime, M. S.; Guo, C. H.; Ekinci, O.; Ghafur, S.; Darzi, A.
Title: Evaluation of a clinical decision support tool for matching cancer patients to clinical trials using simulation-based research
  • Cord-id: h1gbuu69
  • Document date: 2021_6_1
  • ID: h1gbuu69
    Snippet: Simulation-based research (SBR) methods have been proposed as an alternative methodology for evaluating digital health solutions; however, applicability remains to be established. This study used SBR to evaluate a clinical decision support (CDS) tool used for matching cancer patients to clinical trials. 25 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 sig
    Document: Simulation-based research (SBR) methods have been proposed as an alternative methodology for evaluating digital health solutions; however, applicability remains to be established. This study used SBR to evaluate a clinical decision support (CDS) tool used for matching cancer patients to clinical trials. 25 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 evaluating digital health solutions.

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