Dept. of Biomedical Informatics, School of Medicine, Vanderbilt University, Nashville, TN, USA.
Institute for Public Health and Medicine, Northwestern University, Chicago, IL, USA.
J Biomed Inform. 2018 Jan;77:1-10. doi: 10.1016/j.jbi.2017.11.014. Epub 2017 Nov 22.
The traditional fee-for-service approach to healthcare can lead to the management of a patient's conditions in a siloed manner, inducing various negative consequences. It has been recognized that a bundled approach to healthcare - one that manages a collection of health conditions together - may enable greater efficacy and cost savings. However, it is not always evident which sets of conditions should be managed in a bundled manner. In this study, we investigate if a data-driven approach can automatically learn potential bundles.
We designed a framework to infer health condition collections (HCCs) based on the similarity of their clinical workflows, according to electronic medical record (EMR) utilization. We evaluated the framework with data from over 16,500 inpatient stays from Northwestern Memorial Hospital in Chicago, Illinois. The plausibility of the inferred HCCs for bundled care was assessed through an online survey of a panel of five experts, whose responses were analyzed via an analysis of variance (ANOVA) at a 95% confidence level. We further assessed the face validity of the HCCs using evidence in the published literature.
The framework inferred four HCCs, indicative of (1) fetal abnormalities, (2) late pregnancies, (3) prostate problems, and (4) chronic diseases, with congestive heart failure featuring prominently. Each HCC was substantiated with evidence in the literature and was deemed plausible for bundled care by the experts at a statistically significant level.
The findings suggest that an automated EMR data-driven framework conducted can provide a basis for discovering bundled care opportunities. Still, translating such findings into actual care management will require further refinement, implementation, and evaluation.
传统的按服务收费的医疗保健方法可能导致以孤立的方式管理患者的病情，从而产生各种负面后果。人们已经认识到，对医疗保健采用捆绑式方法 - 即共同管理一系列健康状况 - 可能会提高疗效并节省成本。但是，并不总是很清楚哪些条件集应该以捆绑方式进行管理。在这项研究中，我们研究了数据驱动的方法是否可以自动学习潜在的捆绑。
我们设计了一个框架，根据电子病历（EMR）利用情况，根据临床工作流程的相似性推断健康状况集合（HCC）。我们使用来自伊利诺伊州芝加哥西北纪念医院的超过 16500 次住院治疗的数据评估了该框架。通过对五名专家组成的小组进行在线调查来评估推断的 HCC 进行捆绑护理的合理性，其响应通过方差分析（ANOVA）在 95％置信水平下进行分析。我们还使用已发表文献中的证据评估了 HCC 的表面有效性。
该框架推断出了四个 HCC，分别表示（1）胎儿异常，（2）晚期妊娠，（3）前列腺问题和（4）慢性疾病，心力衰竭突出。每个 HCC 都有文献中的证据支持，并被专家认为在统计学上具有捆绑护理的合理性。
这些发现表明，自动化的 EMR 数据驱动框架可以为发现捆绑护理机会提供基础。但是，将这些发现转化为实际的护理管理仍需要进一步的改进，实施和评估。
