Gabrielle Safian, Kaiyu Tio, Kevin Tang, Shitij Arora, Sunit Jariwala
{"title":"EHR Use in Inpatient Physicians: Patterns and Predictors.","authors":"Gabrielle Safian, Kaiyu Tio, Kevin Tang, Shitij Arora, Sunit Jariwala","doi":"10.1055/a-2615-4085","DOIUrl":null,"url":null,"abstract":"<p><p>Electronic health record (EHR) systems have become integral to hospital-based care, with studies showing physicians spending significant time interfacing with these systems. While EHR interactions are necessary for patient care, understanding usage patterns can identify opportunities for system optimization and workflow improvement. Previous studies have focused on outpatient settings, making this study among the first to comprehensively analyze inpatient EHR interaction patterns.This study aims to characterize EHR utilization patterns among inpatient physicians and analyze how these patterns vary by physician characteristics, including gender, specialty, and years of experience. This analysis aims to identify opportunities for targeted EHR optimization and workflow enhancement strategies.We analyzed nine key EHR interaction metrics from Epic Signal across 1,787 inpatient physicians during February 2024. Metrics included time spent in various EHR activities, patient volume, secure message usage, and specific feature utilization. Multivariate regression models were then generated for each outcome metric.Female physicians spent more time per patient in the EHR (21.74 vs. 15.62 minutes, <i>p</i> < 0.001) and utilized secure messaging features more frequently (messages sent: 0.82 vs. 0.06 per day, <i>p</i> < 0.001). Internal Medicine/Pediatrics demonstrated higher EHR interaction times across multiple metrics compared to Surgical Specialists, even after adjusting for patient load (51.93 vs. 8.37 minutes per day, <i>p</i> < 0.001). Years since graduation showed significant negative correlations with most EHR interaction metrics (<i>r</i> = -0.11 to -0.27, <i>p</i> < 0.001).This analysis reveals significant variation in EHR utilization patterns across physician demographics and specialties. These findings can inform targeted interventions to optimize EHR workflows and support efficient system usage while maintaining documentation quality and patient care standards.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":"16 4","pages":"1271-1280"},"PeriodicalIF":2.2000,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12494415/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Clinical Informatics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1055/a-2615-4085","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/10/3 0:00:00","PubModel":"Epub","JCR":"Q4","JCRName":"MEDICAL INFORMATICS","Score":null,"Total":0}
引用次数: 0
Abstract
Electronic health record (EHR) systems have become integral to hospital-based care, with studies showing physicians spending significant time interfacing with these systems. While EHR interactions are necessary for patient care, understanding usage patterns can identify opportunities for system optimization and workflow improvement. Previous studies have focused on outpatient settings, making this study among the first to comprehensively analyze inpatient EHR interaction patterns.This study aims to characterize EHR utilization patterns among inpatient physicians and analyze how these patterns vary by physician characteristics, including gender, specialty, and years of experience. This analysis aims to identify opportunities for targeted EHR optimization and workflow enhancement strategies.We analyzed nine key EHR interaction metrics from Epic Signal across 1,787 inpatient physicians during February 2024. Metrics included time spent in various EHR activities, patient volume, secure message usage, and specific feature utilization. Multivariate regression models were then generated for each outcome metric.Female physicians spent more time per patient in the EHR (21.74 vs. 15.62 minutes, p < 0.001) and utilized secure messaging features more frequently (messages sent: 0.82 vs. 0.06 per day, p < 0.001). Internal Medicine/Pediatrics demonstrated higher EHR interaction times across multiple metrics compared to Surgical Specialists, even after adjusting for patient load (51.93 vs. 8.37 minutes per day, p < 0.001). Years since graduation showed significant negative correlations with most EHR interaction metrics (r = -0.11 to -0.27, p < 0.001).This analysis reveals significant variation in EHR utilization patterns across physician demographics and specialties. These findings can inform targeted interventions to optimize EHR workflows and support efficient system usage while maintaining documentation quality and patient care standards.
电子健康记录(EHR)系统已经成为医院护理不可或缺的一部分,研究表明医生花费大量时间与这些系统交互。虽然EHR交互对于患者护理是必要的,但了解使用模式可以确定系统优化和工作流程改进的机会。先前的研究主要集中在门诊设置,使本研究成为第一个全面分析住院患者电子病历互动模式的研究。本研究旨在描述住院医生的电子病历使用模式,并分析这些模式如何随医生特征(包括性别、专业和经验)而变化。该分析旨在确定有针对性的EHR优化和工作流增强策略的机会。我们分析了Epic Signal在2024年2月期间对1,787名住院医生的9个关键电子病历交互指标。指标包括在各种EHR活动中花费的时间、患者数量、安全消息使用情况和特定功能利用率。然后为每个结果指标生成多元回归模型。女医生花在每位患者EHR上的时间更多(21.74分钟vs 15.62分钟,p p p r = -0.11 ~ -0.27, p
期刊介绍:
ACI is the third Schattauer journal dealing with biomedical and health informatics. It perfectly complements our other journals Öffnet internen Link im aktuellen FensterMethods of Information in Medicine and the Öffnet internen Link im aktuellen FensterYearbook of Medical Informatics. The Yearbook of Medical Informatics being the “Milestone” or state-of-the-art journal and Methods of Information in Medicine being the “Science and Research” journal of IMIA, ACI intends to be the “Practical” journal of IMIA.