Predicting and Preventing Acute Care Re-Utilization by Patients with Diabetes.
Predicting and Preventing Acute Care Re-Utilization by Patients with Diabetes.
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DOI：
10.1007/s11892-021-01402-7
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发表时间：
2021-09-04
影响因子：
4.2
通讯作者：
Shah AA
中科院分区：
文献类型：
--
作者：
Rubin DJ;Shah AA
急性护理再利用，即医院再入院和出院后急诊科(ED)的使用，是医疗成本的重要驱动因素，也是医疗质量的标志。糖尿病是急性护理再利用和相关成本的主要贡献者。本文的目标是(1)回顾糖尿病患者再入院的流行病学，(2)描述预测再入院风险的模型，以及(3)解决降低急性护理再利用风险的各种策略。糖尿病患者再次住院和急诊室就诊很常见，费用也很高。再入院的主要危险因素包括社会人口学、合并症、胰岛素使用、住院时间(LOS)和再住院史，其中大多数是不可更改的。已经开发了几个用于预测糖尿病患者再入院风险的模型，其中两个在外部验证中具有相当的准确性。在回溯性研究和大多数小型随机对照试验(RCT)中，住院糖尿病教育、住院糖尿病管理服务、护理支持的过渡和门诊随访等干预措施通常与急性护理再利用的风险降低相关。在因新冠肺炎住院的糖尿病患者中，关于再入院风险和降低再入院风险干预措施的数据有限或缺乏。支持出院后电话跟踪的证据是模棱两可的，也是有限的。糖尿病患者的急性护理再利用为提高医疗质量和降低成本提供了一个重要机会。目前可用的预测模型对识别高危患者很有用，但还可以改进。机器学习模型正变得越来越普遍，它有可能产生更准确的急性护理再利用风险预测。需要嵌入电子健康记录系统的工具将再入院风险预测模型转化为临床实践。一些降低风险的干预措施前景看好，但需要在多站点随机对照试验中进行测试，以证明其通用性、可伸缩性和有效性。
Acute care re-utilization, i.e., hospital readmission and post-discharge Emergency Department (ED) use, is a significant driver of healthcare costs and a marker for healthcare quality. Diabetes is a major contributor to acute care re-utilization and associated costs. The goals of this paper are to (1) review the epidemiology of readmissions among patients with diabetes, (2) describe models that predict readmission risk, and (3) address various strategies for reducing the risk of acute care re-utilization. Hospital readmissions and ED visits by diabetes patients are common and costly. Major risk factors for readmission include sociodemographics, comorbidities, insulin use, hospital length of stay (LOS), and history of readmissions, most of which are non-modifiable. Several models for predicting the risk of readmission among diabetes patients have been developed, two of which have reasonable accuracy in external validation. In retrospective studies and mostly small randomized controlled trials (RCTs), interventions such as inpatient diabetes education, inpatient diabetes management services, transition of care support, and outpatient follow-up are generally associated with a reduction in the risk of acute care re-utilization. Data on readmission risk and readmission risk reduction interventions are limited or lacking among patients with diabetes hospitalized for COVID-19. The evidence supporting post-discharge follow-up by telephone is equivocal and also limited. Acute care re-utilization of patients with diabetes presents an important opportunity to improve healthcare quality and reduce costs. Currently available predictive models are useful for identifying higher risk patients but could be improved. Machine learning models, which are becoming more common, have the potential to generate more accurate acute care re-utilization risk predictions. Tools embedded in electronic health record systems are needed to translate readmission risk prediction models into clinical practice. Several risk reduction interventions hold promise but require testing in multi-site RCTs to prove their generalizability, scalability, and effectiveness.
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影响因子：
4
作者：
Zhao H;Tanner S;Golden SH;Fisher SG;Rubin DJ
通讯作者：
Rubin DJ
影响因子：
4.2
作者：
Bhalodkar, Arpita;Sonmez, Halis;Poretsky, Leonid
通讯作者：
Poretsky, Leonid
影响因子：
39
作者：
Donze, Jacques;Aujesky, Drahomir;Schnipper, Jeffrey L.
通讯作者：
Schnipper, Jeffrey L.
影响因子：
16.2
作者：
Benoit, Stephen R.;Hora, Israel;Imperatore, Giuseppina
通讯作者：
Imperatore, Giuseppina
住院糖尿病教育与血糖控制差的患者的住院入院率较低有关。
影响因子：
16.2
作者：
Healy SJ;Black D;Harris C;Lorenz A;Dungan KM
通讯作者：
Dungan KM