Credible practice of modeling and simulation in healthcare: ten rules from a multidisciplinary perspective

Journal of Translational Medicine

Marc Horner¹  Joy P. Ku²  Ahmet Erdemir³  Rajanikanth Vadigepalli⁴  Tina M. Morrison⁵  Jerry G. Myers⁶  Lealem Mulugeta⁷  Grace C. Y. Peng⁸  Andrew Drach⁹  William W. Lytton^1,10 

[1]ANSYS, Inc, 1007 Church Street, Suite 250, 60201, Evanston, IL, USA

[2]Committee on Credible Practice of Modeling, & Simulation in Healthcare, Interagency Modeling and Analysis Group and Multiscale Modeling Consortium, Bethesda, MD, USA

[3]Department of Bioengineering, Clark Center, Stanford University, 318 Campus Drive, 94305-5448, Stanford, CA, USA

[4]Committee on Credible Practice of Modeling, & Simulation in Healthcare, Interagency Modeling and Analysis Group and Multiscale Modeling Consortium, Bethesda, MD, USA

[5]Department of Biomedical Engineering and Computational Biomodeling (CoBi) Core, Lerner Research Institute, Cleveland Clinic, 9500 Euclid Avenue (ND20), 44195, Cleveland, OH, USA

[6]Committee on Credible Practice of Modeling, & Simulation in Healthcare, Interagency Modeling and Analysis Group and Multiscale Modeling Consortium, Bethesda, MD, USA

[7]Department of Pathology, Anatomy and Cell Biology, Daniel Baugh Institute for Functional Genomics/Computational Biology, Thomas Jefferson University, 1020 Locust St, 19107, Philadelphia, PA, USA

[8]Committee on Credible Practice of Modeling, & Simulation in Healthcare, Interagency Modeling and Analysis Group and Multiscale Modeling Consortium, Bethesda, MD, USA

[9]Division of Applied Mechanics, United States Food and Drug Administration, 10903 New Hampshire Avenue, 20993, Silver Spring, MD, USA

[10]Committee on Credible Practice of Modeling, & Simulation in Healthcare, Interagency Modeling and Analysis Group and Multiscale Modeling Consortium, Bethesda, MD, USA

[11]Human Research Program, Cross-Cutting Computational Modeling Project, National Aeronautics and Space Administration - John H. Glenn Research Center, 21000 Brookpark Road, 44135, Cleveland, OH, USA

[12]Committee on Credible Practice of Modeling, & Simulation in Healthcare, Interagency Modeling and Analysis Group and Multiscale Modeling Consortium, Bethesda, MD, USA

[13]InSilico Labs LLC, 2617 Bissonnet St. Suite 435, 77005, Houston, TX, USA

[14]Committee on Credible Practice of Modeling, & Simulation in Healthcare, Interagency Modeling and Analysis Group and Multiscale Modeling Consortium, Bethesda, MD, USA

[15]National Institute of Biomedical Imaging & Bioengineering, Suite 200, MSC 6707 Democracy Blvd5469, 20892, Bethesda, MD, USA

[16]Committee on Credible Practice of Modeling, & Simulation in Healthcare, Interagency Modeling and Analysis Group and Multiscale Modeling Consortium, Bethesda, MD, USA

[17]Oden Institute for Computational Engineering and Sciences, University of Texas at Austin, 201 E. 24th st, 78712, Austin, TX, USA

[18]Committee on Credible Practice of Modeling, & Simulation in Healthcare, Interagency Modeling and Analysis Group and Multiscale Modeling Consortium, Bethesda, MD, USA

[19]State University of New York, Kings County Hospital, 450 Clarkson Ave., MSC 31, 11203, Brooklyn, NY, USA

[20]Committee on Credible Practice of Modeling, & Simulation in Healthcare, Interagency Modeling and Analysis Group and Multiscale Modeling Consortium, Bethesda, MD, USA

关键词: Credibility;    Simulation;    Healthcare;    Verification;    Validation;    Computational modeling;    Computer modeling;    Reliability;    Reproducibility;   

DOI  :  10.1186/s12967-020-02540-4

来源: Springer

【 摘 要 】

The complexities of modern biomedicine are rapidly increasing. Thus, modeling and simulation have become increasingly important as a strategy to understand and predict the trajectory of pathophysiology, disease genesis, and disease spread in support of clinical and policy decisions. In such cases, inappropriate or ill-placed trust in the model and simulation outcomes may result in negative outcomes, and hence illustrate the need to formalize the execution and communication of modeling and simulation practices. Although verification and validation have been generally accepted as significant components of a model’s credibility, they cannot be assumed to equate to a holistic credible practice, which includes activities that can impact comprehension and in-depth examination inherent in the development and reuse of the models. For the past several years, the Committee on Credible Practice of Modeling and Simulation in Healthcare, an interdisciplinary group seeded from a U.S. interagency initiative, has worked to codify best practices. Here, we provide Ten Rules for credible practice of modeling and simulation in healthcare developed from a comparative analysis by the Committee’s multidisciplinary membership, followed by a large stakeholder community survey. These rules establish a unified conceptual framework for modeling and simulation design, implementation, evaluation, dissemination and usage across the modeling and simulation life-cycle. While biomedical science and clinical care domains have somewhat different requirements and expectations for credible practice, our study converged on rules that would be useful across a broad swath of model types. In brief, the rules are: (1) Define context clearly. (2) Use contextually appropriate data. (3) Evaluate within context. (4) List limitations explicitly. (5) Use version control. (6) Document appropriately. (7) Disseminate broadly. (8) Get independent reviews. (9) Test competing implementations. (10) Conform to standards. Although some of these are common sense guidelines, we have found that many are often missed or misconstrued, even by seasoned practitioners. Computational models are already widely used in basic science to generate new biomedical knowledge. As they penetrate clinical care and healthcare policy, contributing to personalized and precision medicine, clinical safety will require established guidelines for the credible practice of modeling and simulation in healthcare.

【 授权许可】

CC BY