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揭示隐藏趋势:在临床记录中识别风险因素的时间轨迹,并预测家庭医疗保健期间的住院和急诊就诊情况。

Uncovering hidden trends: identifying time trajectories in risk factors documented in clinical notes and predicting hospitalizations and emergency department visits during home health care.

机构信息

Columbia University School of Nursing, New York City, New York, USA.

College of Nursing, University of Iowa, Iowa City, Iowa, USA.

出版信息

J Am Med Inform Assoc. 2023 Oct 19;30(11):1801-1810. doi: 10.1093/jamia/ocad101.

DOI:10.1093/jamia/ocad101
PMID:37339524
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10586044/
Abstract

OBJECTIVE

This study aimed to identify temporal risk factor patterns documented in home health care (HHC) clinical notes and examine their association with hospitalizations or emergency department (ED) visits.

MATERIALS AND METHODS

Data for 73 350 episodes of care from one large HHC organization were analyzed using dynamic time warping and hierarchical clustering analysis to identify the temporal patterns of risk factors documented in clinical notes. The Omaha System nursing terminology represented risk factors. First, clinical characteristics were compared between clusters. Next, multivariate logistic regression was used to examine the association between clusters and risk for hospitalizations or ED visits. Omaha System domains corresponding to risk factors were analyzed and described in each cluster.

RESULTS

Six temporal clusters emerged, showing different patterns in how risk factors were documented over time. Patients with a steep increase in documented risk factors over time had a 3 times higher likelihood of hospitalization or ED visit than patients with no documented risk factors. Most risk factors belonged to the physiological domain, and only a few were in the environmental domain.

DISCUSSION

An analysis of risk factor trajectories reflects a patient's evolving health status during a HHC episode. Using standardized nursing terminology, this study provided new insights into the complex temporal dynamics of HHC, which may lead to improved patient outcomes through better treatment and management plans.

CONCLUSION

Incorporating temporal patterns in documented risk factors and their clusters into early warning systems may activate interventions to prevent hospitalizations or ED visits in HHC.

摘要

目的

本研究旨在识别家庭保健(HHC)临床记录中记录的时间风险因素模式,并研究其与住院或急诊就诊的相关性。

材料与方法

对一家大型 HHC 机构的 73350 个护理案例进行了数据分析,使用动态时间 warping 和层次聚类分析来识别临床记录中记录的风险因素的时间模式。奥马哈系统护理术语表示风险因素。首先,对聚类之间的临床特征进行了比较。接下来,使用多变量逻辑回归分析了聚类与住院或急诊就诊风险之间的关联。分析并描述了每个聚类中对应于风险因素的奥马哈系统域。

结果

出现了六个时间聚类,显示了风险因素随时间记录的不同模式。与没有记录风险因素的患者相比,随时间记录的风险因素急剧增加的患者住院或急诊就诊的可能性高 3 倍。大多数风险因素属于生理域,只有少数属于环境域。

讨论

风险因素轨迹的分析反映了患者在 HHC 期间健康状况的变化。本研究使用标准化护理术语,深入了解 HHC 的复杂时间动态,这可能通过更好的治疗和管理计划改善患者的治疗效果。

结论

将记录的风险因素及其聚类中的时间模式纳入早期预警系统,可能会激活干预措施,以预防 HHC 中的住院或急诊就诊。

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本文引用的文献

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Machine learning applied to electronic health record data in home healthcare: A scoping review.机器学习在家庭医疗保健中的电子健康记录数据中的应用:范围综述。
Int J Med Inform. 2023 Feb;170:104978. doi: 10.1016/j.ijmedinf.2022.104978. Epub 2022 Dec 30.
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Using Multi-Dimensional Dynamic Time Warping to Identify Time-Varying Lead-Lag Relationships.利用多维动态时间规整识别时变的领先-滞后关系。
Sensors (Basel). 2022 Sep 12;22(18):6884. doi: 10.3390/s22186884.
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Do nurses document all discussions of patient problems and nursing interventions in the electronic health record? A pilot study in home healthcare.护士是否会在电子健康记录中记录所有关于患者问题和护理干预措施的讨论?一项家庭医疗保健的试点研究。
JAMIA Open. 2022 May 26;5(2):ooac034. doi: 10.1093/jamiaopen/ooac034. eCollection 2022 Jul.
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Statistical power for cluster analysis.聚类分析的统计功效。
BMC Bioinformatics. 2022 May 31;23(1):205. doi: 10.1186/s12859-022-04675-1.
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Clinical notes: An untapped opportunity for improving risk prediction for hospitalization and emergency department visit during home health care.临床记录:改善居家医疗期间住院和急诊就诊风险预测的一个未开发机会。
J Biomed Inform. 2022 Apr;128:104039. doi: 10.1016/j.jbi.2022.104039. Epub 2022 Feb 26.
6
Documentation of hospitalization risk factors in electronic health records (EHRs): a qualitative study with home healthcare clinicians.电子健康记录(EHRs)中住院风险因素的记录:一项针对家庭保健临床医生的定性研究。
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7
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Nurs Res. 2022;71(4):285-294. doi: 10.1097/NNR.0000000000000586. Epub 2022 Feb 16.
8
Extracting social determinants of health from electronic health records using natural language processing: a systematic review.利用自然语言处理从电子健康记录中提取健康的社会决定因素:系统评价。
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The Case Time Series Design.病例时间序列设计。
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