使用医疗保健过程建模方法来理解基于电子健康记录的压力损伤数据,并支持标准化压力损伤表型管道的开发。

AMIA ... Annual Symposium proceedings. AMIA Symposium Pub Date : 2025-05-22 eCollection Date: 2024-01-01
Luwei Liu, Min-Jeoung Kang, Michael Sainlaire, Graham Lowenthal, Tanya Martel, Sandy Cho, Debra Furlong, Wadia Gilles-Fowler, Luciana Schleder Goncalves, Lisa Herlihy, Veysel Karani Baris, Jacqueline Massaro, Beth Melanson, Lori D Morrow, Paula Wolski, Wenyu Song, Patricia C Dykes
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引用次数: 0

摘要

医疗保健过程的复杂性对使用电子健康记录(EHR)数据构建高保真表型提出了重大挑战。本研究利用医疗保健过程建模(HPM)方法来理解构建标准化PrI表型管道所需的基于ehr的压力损伤(PrI)数据模式。PrI HPM是通过临床专家、数据科学家、数据库分析师和信息学家之间的跨学科合作,使用混合方法开发和验证的,包括探索性顺序设计。定性分析确定了PrI护理和相关临床记录过程之间的动态关系。定量分析确定了PrI数据固有的挑战和局限性。PrI HPM包括三个调节因素:系统配置、医院政策和护士个人工作流程。我们进一步将HPM纳入PrI表型发展过程,以解决表型挑战。此外,我们提出了一套标准化的建议,以解决PrI表型挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using a Healthcare Process Modeling Approach to Understand Electronic Health Records-based Pressure Injury Data and to Support Development of a Standardized Pressure Injury Phenotyping Pipeline.

The complexity of health care processes present significant challenges for using Electronic Health Records (EHR) data to build high fidelity phenotypes. This study leverages a healthcare process modeling (HPM) approach to enable understanding of EHR-based pressure injury (PrI) data patterns needed for building a standardized PrI phenotyping pipeline. The PrI HPM was developed and validated using mixed methods, including exploratory sequential design, through interdisciplinary collaboration among clinical experts, data scientists, database analysts, and informaticians. zThe qualitative analysis identified the dynamics between PrI care and the associated clinical documentation processes. The quantitative analysis identified inherent challenges and limitations of the PrI data. The PrI HPM includes three moderating factors: system configuration, hospital policy, and nurse's individual workflow. We further incorporated the HPM into the PrI phenotype development process to address phenotyping challenges. Moreover, we suggested a set of standardizable recommendations to address PrI phenotyping challenges.

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