Optimizing the Implementation of Clinical Predictive Models to Minimize National Costs: Sepsis Case Study.
Optimizing the Implementation of Clinical Predictive Models to Minimize National Costs: Sepsis Case Study.
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优化临床预测模型的实施以降低国家成本：脓毒症案例研究
DOI：
10.2196/43486
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发表时间：
2023-02-13
影响因子：
7.4
通讯作者：
Nemati, Shamim
中科院分区：
文献类型：
--
作者：
Rogers, Parker;Boussina, Aaron E.;Shashikumar, Supreeth P.;Wardi, Gabriel;Longhurst, Christopher A.;Nemati, Shamim
关键词：
脓毒症的成本和发病率在诊断类别中差异很大，这就要求采用定制的方法来实现预测模型。本研究的目的是在不同的患者组内优化脓毒症预测模型的参数，以最大限度地减少脓毒症护理的额外成本，并分析导致最终用户对脓毒症警报的反应的因素对整体模型效用的潜在影响。我们通过比较有和没有二次败血症诊断但有相同的主要诊断和基线合并症的患者，计算了败血症给医疗保险和医疗补助服务中心（CMS）带来的额外费用。我们优化了不同诊断类别的脓毒症预测算法的参数，以最大限度地减少这些额外成本。在最佳情况下，我们评估了诊断优势比，并分析了依从性因素（如不依从性、治疗疗效和对假警报的耐受性）对触发脓毒症警报的净获益的影响。依从性因素显著促进了触发脓毒症警报的净获益。然而，定制的部署策略可以实现显着更高的诊断优势比和降低败血症护理成本。使用强大的预测模型实施我们的优化程序可以为CMS节省46亿美元的额外成本。我们设计了一个框架，用于在不同的诊断类别中定制脓毒症警报协议，以最大限度地减少多余的成本，并分析了模型性能作为假警报容忍度和模型建议依从性的函数。我们提供了一个框架，CMS政策制定者可以用来推荐最低的坚持率，以早期识别和适当的照顾败血症是敏感的医院部门一级的发病率和国家超额成本。通过考虑各种行为和经济因素来定制临床预测模型的实现可以提高预测模型的实际益处。
Sepsis costs and incidence vary dramatically across diagnostic categories, warranting a customized approach for implementing predictive models. The aim of this study was to optimize the parameters of a sepsis prediction model within distinct patient groups to minimize the excess cost of sepsis care and analyze the potential effect of factors contributing to end-user response to sepsis alerts on overall model utility. We calculated the excess costs of sepsis to the Centers for Medicare and Medicaid Services (CMS) by comparing patients with and without a secondary sepsis diagnosis but with the same primary diagnosis and baseline comorbidities. We optimized the parameters of a sepsis prediction algorithm across different diagnostic categories to minimize these excess costs. At the optima, we evaluated diagnostic odds ratios and analyzed the impact of compliance factors such as noncompliance, treatment efficacy, and tolerance for false alarms on the net benefit of triggering sepsis alerts. Compliance factors significantly contributed to the net benefit of triggering a sepsis alert. However, a customized deployment policy can achieve a significantly higher diagnostic odds ratio and reduced costs of sepsis care. Implementing our optimization routine with powerful predictive models could result in US $4.6 billion in excess cost savings for CMS. We designed a framework for customizing sepsis alert protocols within different diagnostic categories to minimize excess costs and analyzed model performance as a function of false alarm tolerance and compliance with model recommendations. We provide a framework that CMS policymakers could use to recommend minimum adherence rates to the early recognition and appropriate care of sepsis that is sensitive to hospital department-level incidence rates and national excess costs. Customizing the implementation of clinical predictive models by accounting for various behavioral and economic factors may improve the practical benefit of predictive models.
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影响因子：
15.2
作者：
Brant EB;Kennedy JN;King AJ;Gerstley LD;Mishra P;Schlessinger D;Shalaby J;Escobar GJ;Angus DC;Seymour CW;Liu VX
通讯作者：
Liu VX
影响因子：
9.7
作者：
Bates, David W.;Saria, Suchi;Escobar, Gabriel
通讯作者：
Escobar, Gabriel
影响因子：
8.8
作者：
Kumar, Arland;Roberts, Daniel;Cheang, Mary
通讯作者：
Cheang, Mary
DOI：
10.3390/healthcare9121632
发表时间：
2021-11-25
期刊：
Healthcare (Basel, Switzerland)
影响因子：
--
作者：
Islam MM;Li GH;Poly TN;Li YJ
通讯作者：
Li YJ
影响因子：
8.8
作者：
Leisman, Daniel E.;Doerfler, Martin E.;D'Amore, Jason A.
通讯作者：
D'Amore, Jason A.