在电子病历数据上应用时序图分析方法探讨医护人员与患者之间的互动强度:一项队列研究。

IF 4.1 Q1 HEALTH CARE SCIENCES & SERVICES
John Booth, Maria H Eriksson, Stephen D Marks, William A Bryant, Spiros Denaxas, Rebecca Pope, Neil J Sebire
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引用次数: 0

摘要

目的:入院期间患者与医疗保健专业人员(HCP)之间的互动非常复杂,传统的电子病历(EPR)数据分析方法很难对其进行分析。本研究的目的是确定将时态网络分析应用于 EPR 数据的可行性,重点关注医护人员与患者之间随时间变化的互动:方法:将网络(图)分析应用于 EPR 日常收集的结构化数据,以了解 2019 年 5 月至 2023 年 6 月期间肾移植患者入院期间 HCP 与单个患者的互动情况。根据患者是在重症监护室(ICU)还是在标准病房,在一个疗程内按入院日构建网络。医护人员之间的连接以 60 分钟为一个周期。结果:从 127 个肾移植住院病例中构建了 2300 个单个网络。每个网络的节点或 HCP 数量从 2 个到 45 个不等,网络指标提供了有关密度和跨度变化、不同直径和半径的结构变化以及中心化变化的详细信息。每个网络分析指标都有助于描述日常 HCP 网络的动态,而综合研究结果提供了标准方法无法确定的见解:网络分析提供了一种新颖的方法,用于调查和可视化医护人员与患者之间的互动模式,从而更深入地了解医院患者护理的复杂性,并在实际操作中得到广泛应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Method to apply temporal graph analysis on electronic patient record data to explore healthcare professional-patient interaction intensity: a cohort study.

Aim: Interactions between patients and healthcare professionals (HCP) during hospital admissions are complex and difficult to interrogate using traditional analysis of electronic patient record (EPR) data. The objective of this study was to determine the feasibility of applying temporal network analytics to EPR data, focusing on HCP-patient interactions over time.

Method: Network (graph) analysis was applied to routinely collected structured data from an EPR for HCP interactions with individual patients during admissions for patients undergoing renal transplantation between May 2019 and June 2023. Networks were constructed per day of admission within a session, defined by whether the patient was in the intensive care unit (ICU) or standard hospital ward. Connections between HCP were defined using a 60 min period. Reports were generated visualising daily interaction network structures, across individual admissions.

Results: 2300 individual networks were constructed from 127 hospital admissions for renal transplantation. The number of nodes or HCP per network varied from 2 to 45, and network metrics provided detail regarding variation in the density and transitivity, changes in structure with different diameters and radii, and variations in centralisation. Each network analysis metric has a contribution to play in describing the dynamics of a daily HCP network and the composite findings provide insights that cannot be determined with standard approaches.

Conclusions: Network analysis provides a novel approach to investigate and visualise patterns of HCP-patient interactions which allow for a deeper understanding of the complex nature of hospital patient care and could have numerous practical operational applications.

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来源期刊
CiteScore
6.10
自引率
4.90%
发文量
40
审稿时长
18 weeks
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