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Related Experiment Video

Updated: Mar 27, 2026

Author Spotlight: Evaluating Clinicians' Adoption of Ultrasound-Guided Vascular Cannulation Through Simulation Training
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In Silico Evaluation of Algorithm-Based Clinical Decision Support Systems Based on Care Pathway Simulation Models:

Michael Dorosan1, Ya-Lin Chen2,3, Yan He4

  • 1Health Services Research Centre, SingHealth, Academia, Ngee Ann Kongsi Discovery Tower Level 6, 20 College Road, Singapore, 169856, Singapore, 65 65767140.

JMIR AI
|March 24, 2026
PubMed
Summary
This summary is machine-generated.

In silico evaluation (ISE) methods simulate care pathways to assess clinical decision support systems (CDSS). While ISE shows promise for pre-implementation assessment, current methods lack focus on care provider well-being.

Keywords:
ISEalgorithm-basedclinical decisionclinical decision support algorithmsclinical decision-makingclinical workflow simulationdecision supportdecision-makingdescriptive analysisdigital twinhealth care modelingscoping reviewscreeningsupport systemthematic analysisworkflow

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Area of Science:

  • Health Informatics
  • Computational Medicine
  • Health Services Research

Background:

  • In silico evaluation (ISE) creates digital twins of care pathways for assessing clinical decision support systems (CDSS).
  • Despite numerous CDSS studies, fewer than 3% utilize ISE for evaluating broader clinical pathway impacts.
  • A significant increase in ISE application for CDSS evaluation is observed, particularly post-2019.

Purpose of the Study:

  • To review the scope of ISE methods for CDSS evaluation.
  • Focus on simulation modeling, care pathway parameters, and outcomes within ISE.
  • Identify gaps and potential of ISE in strengthening pre-implementation evidence for CDSS.

Main Methods:

  • Scoping review following established methodological guidelines.
  • Tailored search framework and a two-stage screening process for study identification.
  • Extraction of CDSS characteristics and care pathway simulation modeling applications.

Main Results:

  • Most ISE studies focused on patient, process, and cost-effectiveness outcomes.
  • A critical gap exists in evaluating outcomes related to care provider well-being.
  • Simulation approaches included dynamic and state-based models, categorized as decoupled or integrated.

Conclusions:

  • ISE offers a resource-efficient, systems-level evaluation for CDSS before implementation.
  • Care pathway simulation-based ISE strengthens pre-implementation evidence.
  • ISE bridges model performance and clinical workflow impact for contextualized assessments.