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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...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
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.
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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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