Characterizing electronic health record usage patterns of inpatient medicine residents using event log data
Amid growing rates of burnout, physicians report increasing electronic health record (EHR) usage alongside decreasing clinical facetime with patients. There exists a pressing need to improve physician-computer-patient interactions by streamlining EHR workflow. To identify interventions to improve EH...
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Published in | PloS one Vol. 14; no. 2; p. e0205379 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
Published |
United States
Public Library of Science
06.02.2019
Public Library of Science (PLoS) |
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Abstract | Amid growing rates of burnout, physicians report increasing electronic health record (EHR) usage alongside decreasing clinical facetime with patients. There exists a pressing need to improve physician-computer-patient interactions by streamlining EHR workflow. To identify interventions to improve EHR design and usage, we systematically characterize EHR activity among internal medicine residents at a tertiary academic hospital across various inpatient rotations and roles from June 2013 to November 2016. Logged EHR timestamps were extracted from Stanford Hospital's EHR system (Epic) and cross-referenced against resident rotation schedules. We tracked the quantity of EHR logs across 24-hour cycles to reveal daily usage patterns. In addition, we decomposed daily EHR time into time spent on specific EHR actions (e.g. chart review, note entry and review, results review).In examining 24-hour usage cycles from general medicine day and night team rotations, we identified a prominent trend in which night team activity promptly ceased at the shift's end, while day team activity tended to linger post-shift. Across all rotations and roles, residents spent on average 5.38 hours (standard deviation = 2.07) using the EHR. PGY1 (post-graduate year one) interns and PGY2+ residents spent on average 2.4 and 4.1 times the number of EHR hours on information review (chart, note, and results review) as information entry (note and order entry).Analysis of EHR event log data can enable medical educators and programs to develop more targeted interventions to improve physician-computer-patient interactions, centered on specific EHR actions. |
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AbstractList | Amid growing rates of burnout, physicians report increasing electronic health record (EHR) usage alongside decreasing clinical facetime with patients. There exists a pressing need to improve physician-computer-patient interactions by streamlining EHR workflow. To identify interventions to improve EHR design and usage, we systematically characterize EHR activity among internal medicine residents at a tertiary academic hospital across various inpatient rotations and roles from June 2013 to November 2016. Logged EHR timestamps were extracted from Stanford Hospital's EHR system (Epic) and cross-referenced against resident rotation schedules. We tracked the quantity of EHR logs across 24-hour cycles to reveal daily usage patterns. In addition, we decomposed daily EHR time into time spent on specific EHR actions (e.g. chart review, note entry and review, results review).In examining 24-hour usage cycles from general medicine day and night team rotations, we identified a prominent trend in which night team activity promptly ceased at the shift's end, while day team activity tended to linger post-shift. Across all rotations and roles, residents spent on average 5.38 hours (standard deviation = 2.07) using the EHR. PGY1 (post-graduate year one) interns and PGY2+ residents spent on average 2.4 and 4.1 times the number of EHR hours on information review (chart, note, and results review) as information entry (note and order entry).Analysis of EHR event log data can enable medical educators and programs to develop more targeted interventions to improve physician-computer-patient interactions, centered on specific EHR actions. |
Audience | Academic |
Author | Hom, Jason Chi, Jeffrey Chen, Jonathan H Wang, Jason K Ouyang, David |
AuthorAffiliation | 1 Mathematical and Computational Science Program, Stanford University, Stanford, California, United States of America University of Alabama at Birmingham, UNITED STATES 2 Department of Medicine, Stanford University, Stanford, California, United States of America |
AuthorAffiliation_xml | – name: 2 Department of Medicine, Stanford University, Stanford, California, United States of America – name: University of Alabama at Birmingham, UNITED STATES – name: 1 Mathematical and Computational Science Program, Stanford University, Stanford, California, United States of America |
Author_xml | – sequence: 1 givenname: Jason K surname: Wang fullname: Wang, Jason K organization: Mathematical and Computational Science Program, Stanford University, Stanford, California, United States of America – sequence: 2 givenname: David surname: Ouyang fullname: Ouyang, David organization: Department of Medicine, Stanford University, Stanford, California, United States of America – sequence: 3 givenname: Jason surname: Hom fullname: Hom, Jason organization: Department of Medicine, Stanford University, Stanford, California, United States of America – sequence: 4 givenname: Jeffrey surname: Chi fullname: Chi, Jeffrey organization: Department of Medicine, Stanford University, Stanford, California, United States of America – sequence: 5 givenname: Jonathan H orcidid: 0000-0002-4387-8740 surname: Chen fullname: Chen, Jonathan H organization: Department of Medicine, Stanford University, Stanford, California, United States of America |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/30726208$$D View this record in MEDLINE/PubMed |
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Copyright | COPYRIGHT 2019 Public Library of Science 2019 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2019 Wang et al 2019 Wang et al |
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Snippet | Amid growing rates of burnout, physicians report increasing electronic health record (EHR) usage alongside decreasing clinical facetime with patients. There... |
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SubjectTerms | Academic Medical Centers Analysis Biology and Life Sciences Burnout Computer and Information Sciences Education Education policy Electronic Health Records Electronic medical records Electronic records Family medical history Health Humans Informatics Inpatients Internal Medicine Internship and Residency Medical personnel Medical records Medicine Medicine and Health Sciences Night People and Places Photoperiod Physician-patient relations Physicians Practice Patterns, Physicians Primary care R&D Research & development Research and Analysis Methods Schedules Social Sciences Streamlining Tertiary Care Centers Time Factors Use statistics Workflow Workflow software |
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Title | Characterizing electronic health record usage patterns of inpatient medicine residents using event log data |
URI | https://www.ncbi.nlm.nih.gov/pubmed/30726208 https://www.proquest.com/docview/2176706712 https://search.proquest.com/docview/2185569065 https://pubmed.ncbi.nlm.nih.gov/PMC6364867 https://doaj.org/article/249db7f9936e4122b6806a4e711d824f http://dx.doi.org/10.1371/journal.pone.0205379 |
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