Erdemir Ahmet, Mulugeta Lealem, Ku Joy P, Drach Andrew, Horner Marc, Morrison Tina M, Peng Grace C Y, Vadigepalli Rajanikanth, Lytton William W, Myers Jerry G
Department of Biomedical Engineering and Computational Biomodeling (CoBi) Core, Lerner Research Institute, Cleveland Clinic, 9500 Euclid Avenue (ND20), Cleveland, OH, 44195, USA.
Committee on Credible Practice of Modeling, & Simulation in Healthcare, Interagency Modeling and Analysis Group and Multiscale Modeling Consortium, Bethesda, MD, USA.
J Transl Med. 2020 Sep 29;18(1):369. doi: 10.1186/s12967-020-02540-4.
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.
现代生物医学的复杂性正在迅速增加。因此，建模与仿真作为一种理解和预测病理生理学、疾病发生及疾病传播轨迹以支持临床和政策决策的策略，变得越来越重要。在这种情况下，对模型和仿真结果不恰当或错误的信任可能会导致负面结果，从而说明需要规范建模与仿真实践的执行和交流。尽管验证和确认已被普遍视为模型可信度的重要组成部分，但不能假定它们等同于全面的可信实践，全面的可信实践还包括可能影响对模型开发和重用中固有理解及深入审查的活动。在过去几年中，医疗保健建模与仿真可信实践委员会（一个源自美国跨部门倡议的跨学科小组）一直致力于编纂最佳实践。在此，我们提供由该委员会多学科成员进行比较分析并经大型利益相关者社区调查后得出的医疗保健建模与仿真可信实践十条规则。这些规则为建模与仿真生命周期内的建模与仿真设计、实施、评估、传播及使用建立了统一的概念框架。虽然生物医学科学和临床护理领域对可信实践有一些不同的要求和期望，但我们的研究得出了适用于广泛模型类型的规则。简而言之，这些规则是：（1）清晰定义背景。（2）使用符合背景的数据。（3）在背景内进行评估。（4）明确列出局限性。（5）使用版本控制。（6）进行适当记录。（7）广泛传播。（8）获得独立评审。（9）测试竞争性实现。（10）符合标准。尽管其中一些是常识性指南，但我们发现即使是经验丰富的从业者也常常会忽略或误解许多规则。计算模型已在基础科学中广泛用于生成新的生物医学知识。随着它们渗透到临床护理和医疗保健政策中，为个性化和精准医学做出贡献，临床安全将需要建立医疗保健建模与仿真可信实践的既定指南。
