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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6364867/ https://www.ncbi.nlm.nih.gov/pubmed/30726208 http://dx.doi.org/10.1371/journal.pone.0205379 |
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author | Wang, Jason K. Ouyang, David Hom, Jason Chi, Jeffrey Chen, Jonathan H. |
author_facet | Wang, Jason K. Ouyang, David Hom, Jason Chi, Jeffrey Chen, Jonathan H. |
author_sort | Wang, Jason K. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6364867 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-63648672019-02-22 Characterizing electronic health record usage patterns of inpatient medicine residents using event log data Wang, Jason K. Ouyang, David Hom, Jason Chi, Jeffrey Chen, Jonathan H. PLoS One Research Article 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. Public Library of Science 2019-02-06 /pmc/articles/PMC6364867/ /pubmed/30726208 http://dx.doi.org/10.1371/journal.pone.0205379 Text en © 2019 Wang et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Wang, Jason K. Ouyang, David Hom, Jason Chi, Jeffrey Chen, Jonathan H. Characterizing electronic health record usage patterns of inpatient medicine residents using event log data |
title | Characterizing electronic health record usage patterns of inpatient medicine residents using event log data |
title_full | Characterizing electronic health record usage patterns of inpatient medicine residents using event log data |
title_fullStr | Characterizing electronic health record usage patterns of inpatient medicine residents using event log data |
title_full_unstemmed | Characterizing electronic health record usage patterns of inpatient medicine residents using event log data |
title_short | Characterizing electronic health record usage patterns of inpatient medicine residents using event log data |
title_sort | characterizing electronic health record usage patterns of inpatient medicine residents using event log data |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6364867/ https://www.ncbi.nlm.nih.gov/pubmed/30726208 http://dx.doi.org/10.1371/journal.pone.0205379 |
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