{"title":"Characterizing electronic messaging use among hospitalists and its association with patient volumes.","authors":"Claire Brickson, Angela Keniston, Michelle Knees, Marisha Burden","doi":"10.1002/jhm.13462","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Secure electronic messaging is increasingly being utilized for communications in healthcare settings. While it likely increases efficiency, it has also been associated with interruptions, high message volumes, and risk of errors due to multitasking.</p><p><strong>Objectives: </strong>We aimed to characterize patterns of secure messaging among hospitalists to understand the volume of messages, message patterns, and impact on hospitalist workload.</p><p><strong>Methods: </strong>This was a retrospective cross-sectional study of Epic Secure Chat secure electronic messages received and sent by hospitalists from April 1 to April 30, 2023 at a large academic medical center. Number of conversations per day, number of chats sent and accessed per hour, and average minutes between when a chat was sent and accessed (lag time) were analyzed using a Pearson correlation coefficient test. Measures were plotted against patient volume and time of day.</p><p><strong>Results: </strong>Hospitalists sent or received an average of 130 messages per day with an average of 13 messages sent or received per hour. The median lag time was 39 s. There was a statistically significant correlation between hospital medicine morning census and number of conversations per day, number of chats sent per hour, and number of chats accessed per hour, but census did not impact lag time.</p><p><strong>Conclusion: </strong>Secure messaging volumes may be higher than previously reported, which may affect hospitalist workload and workflow and have unintended effects on interruptions, multitasking, and medical errors. Additional work should be done to better understand local messaging patterns and opportunities to optimize volume of work and distractions.</p>","PeriodicalId":94084,"journal":{"name":"Journal of hospital medicine","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of hospital medicine","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1002/jhm.13462","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Background: Secure electronic messaging is increasingly being utilized for communications in healthcare settings. While it likely increases efficiency, it has also been associated with interruptions, high message volumes, and risk of errors due to multitasking.
Objectives: We aimed to characterize patterns of secure messaging among hospitalists to understand the volume of messages, message patterns, and impact on hospitalist workload.
Methods: This was a retrospective cross-sectional study of Epic Secure Chat secure electronic messages received and sent by hospitalists from April 1 to April 30, 2023 at a large academic medical center. Number of conversations per day, number of chats sent and accessed per hour, and average minutes between when a chat was sent and accessed (lag time) were analyzed using a Pearson correlation coefficient test. Measures were plotted against patient volume and time of day.
Results: Hospitalists sent or received an average of 130 messages per day with an average of 13 messages sent or received per hour. The median lag time was 39 s. There was a statistically significant correlation between hospital medicine morning census and number of conversations per day, number of chats sent per hour, and number of chats accessed per hour, but census did not impact lag time.
Conclusion: Secure messaging volumes may be higher than previously reported, which may affect hospitalist workload and workflow and have unintended effects on interruptions, multitasking, and medical errors. Additional work should be done to better understand local messaging patterns and opportunities to optimize volume of work and distractions.