Data-driven decisions for reducing readmissions for heart failure: general methodology and case study.
Data-driven decisions for reducing readmissions for heart failure: general methodology and case study.
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DOI：
10.1371/journal.pone.0109264
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发表时间：
2014
期刊：
影响因子：
3.7
通讯作者：
Horvitz E
中科院分区：
文献类型：
--
作者：
Bayati M;Braverman M;Gillam M;Mack KM;Ruiz G;Smith MS;Horvitz E
有几项研究的重点是根据患者的再入院风险水平对患者进行分层，这在一定程度上是由美国的激励计划推动的，这些计划将再入院率与联邦医疗保险(Medicare)的年度支付更新联系起来。针对患者的再入院预测尚未得到广泛应用，因为它们的准确性有限，而且对使用风险测量来指导临床决策的有效性存在疑问。我们构建了充血性心力衰竭(CHF)再次住院的预测模型，并研究了如何使用其预测来执行针对患者的干预措施。我们评估了一种结合预测和决策来分配干预措施的方法的成本效益。研究结果凸显了预测与决策分析相结合的重要性。我们从793次因心力衰竭住院的回顾数据库中构建了一个统计分类器，预测患者在出院30天内再次住院的可能性。我们介绍了一种决策分析，它使用预测来指导有关出院后干预的决策。我们对379次额外的医院就诊进行了成本效益分析，这些就诊既没有包括在分类器的制定中，也没有包括在决策分析中。我们报告了该方法的性能，并展示了采用实时决策系统的总体预期价值。在所研究的队列中，重新入院与平均费用13,679美元相关，标准误差为1,214美元。考虑到出院后计划的成本为1,300美元，并将30天的再住院人数减少35%，使用拟议的方法将减少18.2%的再住院人数，并节省3.8%的费用。从患者数据中自动学习的分类器可以与决策分析结合起来，以指导为CHF患者分配出院后支持。在为所有患者提供方案在经济上并不可行的常见情况下，这样的分析尤其有价值。
Several studies have focused on stratifying patients according to their level of readmission risk, fueled in part by incentive programs in the U.S. that link readmission rates to the annual payment update by Medicare. Patient-specific predictions about readmission have not seen widespread use because of their limited accuracy and questions about the efficacy of using measures of risk to guide clinical decisions. We construct a predictive model for readmissions for congestive heart failure (CHF) and study how its predictions can be used to perform patient-specific interventions. We assess the cost-effectiveness of a methodology that combines prediction and decision making to allocate interventions. The results highlight the importance of combining predictions with decision analysis. We construct a statistical classifier from a retrospective database of 793 hospital visits for heart failure that predicts the likelihood that patients will be rehospitalized within 30 days of discharge. We introduce a decision analysis that uses the predictions to guide decisions about post-discharge interventions. We perform a cost-effectiveness analysis of 379 additional hospital visits that were not included in either the formulation of the classifiers or the decision analysis. We report the performance of the methodology and show the overall expected value of employing a real-time decision system. For the cohort studied, readmissions are associated with a mean cost of $13,679 with a standard error of $1,214. Given a post-discharge plan that costs $1,300 and that reduces 30-day rehospitalizations by 35%, use of the proposed methods would provide an 18.2% reduction in rehospitalizations and save 3.8% of costs. Classifiers learned automatically from patient data can be joined with decision analysis to guide the allocation of post-discharge support to CHF patients. Such analyses are especially valuable in the common situation where it is not economically feasible to provide programs to all patients.
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影响因子：
2.1
作者：
Jovicic, Aleksandra;Holroyd-Leduc, Jayna M;Straus, Sharon E
通讯作者：
Straus, Sharon E
影响因子：
9.7
作者：
Vaduganathan, Muthiah;Ambrosy, Andrew P.;Gheorghiade, Mihai
通讯作者：
Gheorghiade, Mihai
影响因子：
158.5
作者：
Jencks, Stephen F.;Williams, Mark V.;Coleman, Eric A.
通讯作者：
Coleman, Eric A.
DOI：
10.1016/s0735-1097(01)01699-0
发表时间：
2002-01-02
影响因子：
24
作者：
Krumholz, HM;Amatruda, J;Vaccarino, V
通讯作者：
Vaccarino, V
DOI：
10.1161/circoutcomes.108.802686
发表时间：
2008-09-01
影响因子：
6.9
作者：
Keenan, Patricia S.;Normand, Sharon-Lise T.;Krumholz, Harlan M.
通讯作者：
Krumholz, Harlan M.