A rubric for assessing conformance to the Ten Rules for credible practice of modeling and simulation in healthcare
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Summary
This summary is machine-generated.A new rubric assesses computational modeling and simulation (M&S) against Ten Rules for credibility in healthcare. This tool systematically evaluates M&S activities, enhancing their value for real-world decision-making.
Area Of Science
- Biomedical research and healthcare
- Computational modeling and simulation (M&S)
Background
- Credibility in healthcare M&S is crucial for reliable results and evidence generation.
- The Committee on Credible Practice of Modeling & Simulation in Healthcare developed Ten Rules (TR) to guide M&S practices.
- A multidisciplinary effort since 2012 established these rules for life sciences and healthcare.
Purpose Of The Study
- To introduce a pragmatic rubric for assessing M&S activity conformance to the Ten Rules.
- To provide a uniform approach for comparing M&S studies across different reviewers and contexts.
- To facilitate communication about M&S credibility to diverse stakeholders, including policymakers.
Main Methods
- Developed an ordinal scale (Insufficient to Comprehensive) for evaluating adherence to each of the Ten Rules.
- Applied the rubric to two distinct computational modeling activities: COVID-19 propagation and hepatic glycogenolysis.
- Utilized open-source COVID-19 models for their relevance to rapid decision-making during the pandemic.
Main Results
- The rubric effectively evaluated conformance of M&S activities to the Ten Rules.
- Demonstrated the rubric's applicability in real-world scenarios, bridging scientific M&S with policymaking.
- Highlighted the utility of the rubric for assessing open-source models used in urgent public health situations.
Conclusions
- The Ten Rules rubric is a systematic tool for assessing M&S conformance to good practices.
- The rubric enhances the value of the Ten Rules for supporting evidence-based decision-making in healthcare.
- This framework promotes credible M&S practices essential for advancing biomedical research and healthcare outcomes.
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