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Characterizing physician EHR use with vendor derived data: a feasibility study and cross-sectional analysis
OBJECTIVE: To derive 7 proposed core electronic health record (EHR) use metrics across 2 healthcare systems with different EHR vendor product installations and examine factors associated with EHR time. MATERIALS AND METHODS: A cross-sectional analysis of ambulatory physicians EHR use across the Yale...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8279798/ https://www.ncbi.nlm.nih.gov/pubmed/33822970 http://dx.doi.org/10.1093/jamia/ocab011 |
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author | Melnick, Edward R Ong, Shawn Y Fong, Allan Socrates, Vimig Ratwani, Raj M Nath, Bidisha Simonov, Michael Salgia, Anup Williams, Brian Marchalik, Daniel Goldstein, Richard Sinsky, Christine A |
author_facet | Melnick, Edward R Ong, Shawn Y Fong, Allan Socrates, Vimig Ratwani, Raj M Nath, Bidisha Simonov, Michael Salgia, Anup Williams, Brian Marchalik, Daniel Goldstein, Richard Sinsky, Christine A |
author_sort | Melnick, Edward R |
collection | PubMed |
description | OBJECTIVE: To derive 7 proposed core electronic health record (EHR) use metrics across 2 healthcare systems with different EHR vendor product installations and examine factors associated with EHR time. MATERIALS AND METHODS: A cross-sectional analysis of ambulatory physicians EHR use across the Yale-New Haven and MedStar Health systems was performed for August 2019 using 7 proposed core EHR use metrics normalized to 8 hours of patient scheduled time. RESULTS: Five out of 7 proposed metrics could be measured in a population of nonteaching, exclusively ambulatory physicians. Among 573 physicians (Yale-New Haven N = 290, MedStar N = 283) in the analysis, median EHR-Time(8) was 5.23 hours. Gender, additional clinical hours scheduled, and certain medical specialties were associated with EHR-Time(8) after adjusting for age and health system on multivariable analysis. For every 8 hours of scheduled patient time, the model predicted these differences in EHR time (P < .001, unless otherwise indicated): female physicians +0.58 hours; each additional clinical hour scheduled per month −0.01 hours; practicing cardiology −1.30 hours; medical subspecialties −0.89 hours (except gastroenterology, P = .002); neurology/psychiatry −2.60 hours; obstetrics/gynecology −1.88 hours; pediatrics −1.05 hours (P = .001); sports/physical medicine and rehabilitation −3.25 hours; and surgical specialties −3.65 hours. CONCLUSIONS: For every 8 hours of scheduled patient time, ambulatory physicians spend more than 5 hours on the EHR. Physician gender, specialty, and number of clinical hours practicing are associated with differences in EHR time. While audit logs remain a powerful tool for understanding physician EHR use, additional transparency, granularity, and standardization of vendor-derived EHR use data definitions are still necessary to standardize EHR use measurement. |
format | Online Article Text |
id | pubmed-8279798 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-82797982021-07-15 Characterizing physician EHR use with vendor derived data: a feasibility study and cross-sectional analysis Melnick, Edward R Ong, Shawn Y Fong, Allan Socrates, Vimig Ratwani, Raj M Nath, Bidisha Simonov, Michael Salgia, Anup Williams, Brian Marchalik, Daniel Goldstein, Richard Sinsky, Christine A J Am Med Inform Assoc Research and Applications OBJECTIVE: To derive 7 proposed core electronic health record (EHR) use metrics across 2 healthcare systems with different EHR vendor product installations and examine factors associated with EHR time. MATERIALS AND METHODS: A cross-sectional analysis of ambulatory physicians EHR use across the Yale-New Haven and MedStar Health systems was performed for August 2019 using 7 proposed core EHR use metrics normalized to 8 hours of patient scheduled time. RESULTS: Five out of 7 proposed metrics could be measured in a population of nonteaching, exclusively ambulatory physicians. Among 573 physicians (Yale-New Haven N = 290, MedStar N = 283) in the analysis, median EHR-Time(8) was 5.23 hours. Gender, additional clinical hours scheduled, and certain medical specialties were associated with EHR-Time(8) after adjusting for age and health system on multivariable analysis. For every 8 hours of scheduled patient time, the model predicted these differences in EHR time (P < .001, unless otherwise indicated): female physicians +0.58 hours; each additional clinical hour scheduled per month −0.01 hours; practicing cardiology −1.30 hours; medical subspecialties −0.89 hours (except gastroenterology, P = .002); neurology/psychiatry −2.60 hours; obstetrics/gynecology −1.88 hours; pediatrics −1.05 hours (P = .001); sports/physical medicine and rehabilitation −3.25 hours; and surgical specialties −3.65 hours. CONCLUSIONS: For every 8 hours of scheduled patient time, ambulatory physicians spend more than 5 hours on the EHR. Physician gender, specialty, and number of clinical hours practicing are associated with differences in EHR time. While audit logs remain a powerful tool for understanding physician EHR use, additional transparency, granularity, and standardization of vendor-derived EHR use data definitions are still necessary to standardize EHR use measurement. Oxford University Press 2021-05-04 /pmc/articles/PMC8279798/ /pubmed/33822970 http://dx.doi.org/10.1093/jamia/ocab011 Text en © The Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) ), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Research and Applications Melnick, Edward R Ong, Shawn Y Fong, Allan Socrates, Vimig Ratwani, Raj M Nath, Bidisha Simonov, Michael Salgia, Anup Williams, Brian Marchalik, Daniel Goldstein, Richard Sinsky, Christine A Characterizing physician EHR use with vendor derived data: a feasibility study and cross-sectional analysis |
title | Characterizing physician EHR use with vendor derived data: a feasibility study and cross-sectional analysis |
title_full | Characterizing physician EHR use with vendor derived data: a feasibility study and cross-sectional analysis |
title_fullStr | Characterizing physician EHR use with vendor derived data: a feasibility study and cross-sectional analysis |
title_full_unstemmed | Characterizing physician EHR use with vendor derived data: a feasibility study and cross-sectional analysis |
title_short | Characterizing physician EHR use with vendor derived data: a feasibility study and cross-sectional analysis |
title_sort | characterizing physician ehr use with vendor derived data: a feasibility study and cross-sectional analysis |
topic | Research and Applications |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8279798/ https://www.ncbi.nlm.nih.gov/pubmed/33822970 http://dx.doi.org/10.1093/jamia/ocab011 |
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