测量和可视化医疗保健过程可变性。

IF 4.5 2区医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Biomedical Informatics Pub Date : 2025-09-23 DOI:10.1016/j.jbi.2025.104918

Pengfei Yin , Abel Armas Cervantes , Daniel Capurro

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引用次数: 0

摘要

重要性：了解导致患者护理临床变异性的因素至关重要，因为无根据的变异性可能导致不良事件增加和住院时间延长。确定这种可变性何时变得过度，是优化患者结果和医疗效率的一个步骤。目的：探讨临床变异与临床转归的关系。本研究旨在找出临床变异与住院时间（LOS）之间关系显著的时间点。方法：本队列研究使用MIMIC-IV数据集，收集美国贝斯以色列女执事医疗中心的电子健康记录。我们关注的是接受择期冠状动脉搭桥手术的成年患者，共观察了847例患者。记录年龄、种族、保险类型、Charlson共病指数（CCI）等人口统计学因素。我们进行了变异性分析，其中患者的临床过程表示为事件序列。根据记录的活动起始日对数据进行分割，建立观察窗口。通过回归分析，我们确定了可变性对LOS的影响变得独立显著的时间窗口。结果：回归分析显示，在变异性距离组中排名前20位 %的患者的LOS增加了81 %（95 % CI: 1.72至1.91,p ）。结论：在所研究的队列中，变异性较大的患者路程与较长的LOS相关，并具有剂量-反应关系：变异性越高，LOS越长。本研究提出了一种标准化的方法来测量和可视化临床过程中的变异性，并测量其对患者相关结果的影响。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Measuring and visualizing healthcare process variability

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Measuring and visualizing healthcare process variability

Importance

Understanding factors that contribute to clinical variability in patient care is critical, as unwarranted variability can lead to increased adverse events and prolonged hospital stays. Determining when this variability becomes excessive can be a step in optimizing patient outcomes and healthcare efficiency.

Objective

Explore the association between clinical variation and clinical outcomes. This study aims to identify the point in time when the relationship between clinical variation and length of stay (LOS) becomes significant.

Methods

This cohort study uses MIMIC-IV, a dataset collecting electronic health records of the Beth Israel Deaconess Medical Center in the United States. We focused on adult patients who underwent elective coronary bypass surgery, generating 847 patient observations. Demographic factors such as age, race, insurance type, and the Charlson Comorbidity Index (CCI) were recorded. We performed a variability analysis where patients’ clinical processes are represented as sequences of events. The data was segmented based on the initial day of recorded activity to establish observation windows. Using a regression analysis, we identified the temporal window where variability’s impact on LOS becomes independently significant.

Result

Regression analysis revealed that patients in the top 20 % of the variability distance group experienced an 81 % increase in LOS (95 % CI: 1.72 to 1.91, p < 0.001). Insurance types, such as Medicare and Other, were associated with 18 % (95 % CI: 0.73 to 0.92, p < 0.001) and 21 % (95 % CI: 0.71 to 0.88, p < 0.001) decreases in LOS, respectively. Neither age nor race significantly affected LOS, but a higher CCI was associated with a 3.3 % increase in LOS (95 % CI: 1.02 to 1.05, p < 0.001). These findings indicate that higher variability and CCI significantly influence LOS, with insurance type also playing a crucial role.

Conclusion

In the studied cohort, patient journeys with greater variability were associated with longer LOS with a dose–response relationship: the higher the variability, the longer LOS. This study presents a standardized way to measure and visualize variability in clinical processes and measure its impact on patient-relevant outcomes.

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来源期刊

Journal of Biomedical Informatics 医学-计算机：跨学科应用

CiteScore

8.90

自引率

6.70%

发文量

243

审稿时长

32 days

期刊介绍： The Journal of Biomedical Informatics reflects a commitment to high-quality original research papers, reviews, and commentaries in the area of biomedical informatics methodology. Although we publish articles motivated by applications in the biomedical sciences (for example, clinical medicine, health care, population health, and translational bioinformatics), the journal emphasizes reports of new methodologies and techniques that have general applicability and that form the basis for the evolving science of biomedical informatics. Articles on medical devices; evaluations of implemented systems (including clinical trials of information technologies); or papers that provide insight into a biological process, a specific disease, or treatment options would generally be more suitable for publication in other venues. Papers on applications of signal processing and image analysis are often more suitable for biomedical engineering journals or other informatics journals, although we do publish papers that emphasize the information management and knowledge representation/modeling issues that arise in the storage and use of biological signals and images. System descriptions are welcome if they illustrate and substantiate the underlying methodology that is the principal focus of the report and an effort is made to address the generalizability and/or range of application of that methodology. Note also that, given the international nature of JBI, papers that deal with specific languages other than English, or with country-specific health systems or approaches, are acceptable for JBI only if they offer generalizable lessons that are relevant to the broad JBI readership, regardless of their country, language, culture, or health system.