Credible practice of modeling and simulation in healthcare: ten rules from a multidisciplinary perspective.
Credible practice of modeling and simulation in healthcare: ten rules from a multidisciplinary perspective.
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DOI：
10.1186/s12967-020-02540-4
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发表时间：
2020-09-29
影响因子：
7.4
通讯作者：
Myers JG Jr
中科院分区：
文献类型：
--
作者：
Erdemir A;Mulugeta L;Ku JP;Drach A;Horner M;Morrison TM;Peng GCY;Vadigepalli R;Lytton WW;Myers JG Jr
现代生物医学的复杂性正在迅速增加。因此，建模和仿真作为理解和预测病理生理学、疾病发生和疾病传播的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，对模型和仿真结果的不适当或不恰当的信任可能会导致负面结果，因此说明需要将建模和仿真实践的执行和通信形式化。虽然验证和确认已被普遍接受为模型可信度的重要组成部分，但不能假定它们等同于整体可信实践，其中包括可能影响模型开发和重用中固有的理解和深入检查的活动。在过去的几年里，医疗保健建模与仿真可信实践委员会（Committee on Credible Practice of Modeling and Simulation in Healthcare）是一个由美国跨机构倡议发起的跨学科小组，致力于编纂最佳实践。在这里，我们提供了医疗保健建模和仿真的可信实践的十条规则，这些规则是由委员会多学科成员的比较分析开发的，然后是一个大型利益相关者社区调查。这些规则为建模和仿真的设计、实现、评估、传播和使用建立了一个统一的概念框架。虽然生物医学科学和临床护理领域对可信实践的要求和期望有所不同，但我们的研究集中在对广泛的模型类型有用的规则上。简言之，规则是：（1）明确定义上下文。(2)使用上下文相关的数据。(3)在上下文中进行评估。(4)明确列出限制。(5)使用版本控制。(6)适当记录。(7)广泛传播。(8)获得独立评论。(9)测试竞争的实现。(10)符合标准。虽然其中一些是常识性的指导方针，但我们发现，即使是经验丰富的从业者，也经常错过或误解许多指导方针。计算模型已经广泛用于基础科学，以产生新的生物医学知识。随着它们渗透到临床护理和医疗保健政策中，为个性化和精准医疗做出贡献，临床安全将需要建立医疗保健建模和模拟的可靠实践指南。
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.
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DOI：
10.1109/tbme.2016.2602760
发表时间：
2016-10
期刊：
IEEE transactions on bio-medical engineering
影响因子：
--
作者：
Erdemir A;Guess TM;Halloran JP;Modenese L;Reinbolt JA;Thelen DG;Umberger BR;Erdemir A;Guess TM;Halloran JP;Modenese L;Reinbolt JA;Thelen DG;Umberger BR;Umberger BR;Erdemir A;Thelen DG;Guess TM;Reinbolt JA;Modenese L;Halloran JP
通讯作者：
Halloran JP
DOI：
10.1115/1.4043346
发表时间：
2019-07-01
影响因子：
1.7
作者：
Erdemir, Ahmet;Besier, Thor F.;Shelburne, Kevin B.
通讯作者：
Shelburne, Kevin B.
影响因子：
2.9
作者：
Clayton, Janine Austin
通讯作者：
Clayton, Janine Austin
影响因子：
3.8
作者：
Behrend, Matthew R.;Basanez, Maria-Gloria;de Vlas, Sake J.
通讯作者：
de Vlas, Sake J.
影响因子：
1.1
作者：
Haddad, Tarek;Himes, Adam;Nair, Rajesh
通讯作者：
Nair, Rajesh