{"title":"医院医生使用电子信息的特点及其与病人数量的关系。","authors":"Claire Brickson MD, Angela Keniston PhD, MSPH, Michelle Knees DO, Marisha Burden MD, MBA","doi":"10.1002/jhm.13462","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>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>\n </section>\n \n <section>\n \n <h3> Objectives</h3>\n \n <p>We aimed to characterize patterns of secure messaging among hospitalists to understand the volume of messages, message patterns, and impact on hospitalist workload.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>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>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>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>\n </section>\n \n <section>\n \n <h3> Conclusion</h3>\n \n <p>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>\n </section>\n </div>","PeriodicalId":15883,"journal":{"name":"Journal of hospital medicine","volume":"19 12","pages":"1131-1137"},"PeriodicalIF":2.4000,"publicationDate":"2024-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Characterizing electronic messaging use among hospitalists and its association with patient volumes\",\"authors\":\"Claire Brickson MD, Angela Keniston PhD, MSPH, Michelle Knees DO, Marisha Burden MD, MBA\",\"doi\":\"10.1002/jhm.13462\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Background</h3>\\n \\n <p>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>\\n </section>\\n \\n <section>\\n \\n <h3> Objectives</h3>\\n \\n <p>We aimed to characterize patterns of secure messaging among hospitalists to understand the volume of messages, message patterns, and impact on hospitalist workload.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>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>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>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>\\n </section>\\n \\n <section>\\n \\n <h3> Conclusion</h3>\\n \\n <p>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>\\n </section>\\n </div>\",\"PeriodicalId\":15883,\"journal\":{\"name\":\"Journal of hospital medicine\",\"volume\":\"19 12\",\"pages\":\"1131-1137\"},\"PeriodicalIF\":2.4000,\"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\":\"3\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/jhm.13462\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MEDICINE, GENERAL & INTERNAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of hospital medicine","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/jhm.13462","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MEDICINE, GENERAL & INTERNAL","Score":null,"Total":0}
Characterizing electronic messaging use among hospitalists and its association with patient volumes
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.
期刊介绍:
JHM is a peer-reviewed publication of the Society of Hospital Medicine and is published 12 times per year. JHM publishes manuscripts that address the care of hospitalized adults or children.
Broad areas of interest include (1) Treatments for common inpatient conditions; (2) Approaches to improving perioperative care; (3) Improving care for hospitalized patients with geriatric or pediatric vulnerabilities (such as mobility problems, or those with complex longitudinal care); (4) Evaluation of innovative healthcare delivery or educational models; (5) Approaches to improving the quality, safety, and value of healthcare across the acute- and postacute-continuum of care; and (6) Evaluation of policy and payment changes that affect hospital and postacute care.