BACKGROUND
Integrating algorithm-based clinical decision support (CDS) systems poses significant challenges in evaluating their actual clinical value. Such CDS systems are traditionally assessed via controlled but resource-intensive clinical trials.
OBJECTIVE
This paper presents a review protocol for pre-implementation in silico evaluation methods to enable broadened impact analysis under simulated environments before clinical trials.
METHODS
We propose a scoping review protocol that follows an enhanced Arksey and O’Malley framework and PRISMA-ScR guidelines to investigate the scope and research gaps in the in silico evaluation of algorithm-based CDS models—specifically CDS decision-making endpoints and objectives, evaluation metrics used, and simulation paradigms employed to assess potential impacts.
RESULTS
The databases searched are PubMed, Embase, CINAHL, PsycInfo, Cochrane, IEEEXplore, Web of Science, and arXiv. A two-stage screening process identified pertinent articles. The information extracted from articles is iteratively refined. The review will employ thematic, trend, and descriptive analyses to meet scoping aims.
CONCLUSIONS
The study’s findings will be published and presented in forums combining artificial intelligence and machine learning, clinical decision-making, and health technology impact analysis; ultimately, we aim to bridge the development-deployment gap through in silico evaluation-based potential impact assessments.