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      Metrics for assessing physician activity using electronic health record log data

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          Abstract

          Electronic health record (EHR) log data have shown promise in measuring physician time spent on clinical activities, contributing to deeper understanding and further optimization of the clinical environment. In this article, we propose 7 core measures of EHR use that reflect multiple dimensions of practice efficiency: total EHR time, work outside of work, time on documentation, time on prescriptions, inbox time, teamwork for orders, and an aspirational measure for the amount of undivided attention patients receive from their physicians during an encounter, undivided attention. We also illustrate sample use cases for these measures for multiple stakeholders. Finally, standardization of EHR log data measure specifications, as outlined here, will foster cross-study synthesis and comparative research.

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          Most cited references26

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          Tethered to the EHR: Primary Care Physician Workload Assessment Using EHR Event Log Data and Time-Motion Observations

          PURPOSE Primary care physicians spend nearly 2 hours on electronic health record (EHR) tasks per hour of direct patient care. Demand for non–face-to-face care, such as communication through a patient portal and administrative tasks, is increasing and contributing to burnout. The goal of this study was to assess time allocated by primary care physicians within the EHR as indicated by EHR user-event log data, both during clinic hours (defined as 8:00 am to 6:00 pm Monday through Friday) and outside clinic hours. METHODS We conducted a retrospective cohort study of 142 family medicine physicians in a single system in southern Wisconsin. All Epic (Epic Systems Corporation) EHR interactions were captured from “event logging” records over a 3-year period for both direct patient care and non–face-to-face activities, and were validated by direct observation. EHR events were assigned to 1 of 15 EHR task categories and allocated to either during or after clinic hours. RESULTS Clinicians spent 355 minutes (5.9 hours) of an 11.4-hour workday in the EHR per weekday per 1.0 clinical full-time equivalent: 269 minutes (4.5 hours) during clinic hours and 86 minutes (1.4 hours) after clinic hours. Clerical and administrative tasks including documentation, order entry, billing and coding, and system security accounted for nearly one-half of the total EHR time (157 minutes, 44.2%). Inbox management accounted for another 85 minutes (23.7%). CONCLUSIONS Primary care physicians spend more than one-half of their workday, nearly 6 hours, interacting with the EHR during and after clinic hours. EHR event logs can identify areas of EHR-related work that could be delegated, thus reducing workload, improving professional satisfaction, and decreasing burnout. Direct time-motion observations validated EHR-event log data as a reliable source of information regarding clinician time allocation.
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            The impact of electronic health records on time efficiency of physicians and nurses: a systematic review.

            A systematic review of the literature was performed to examine the impact of electronic health records (EHRs) on documentation time of physicians and nurses and to identify factors that may explain efficiency differences across studies. In total, 23 papers met our inclusion criteria; five were randomized controlled trials, six were posttest control studies, and 12 were one-group pretest-posttest designs. Most studies (58%) collected data using a time and motion methodology in comparison to work sampling (33%) and self-report/survey methods (8%). A weighted average approach was used to combine results from the studies. The use of bedside terminals and central station desktops saved nurses, respectively, 24.5% and 23.5% of their overall time spent documenting during a shift. Using bedside or point-of-care systems increased documentation time of physicians by 17.5%. In comparison, the use of central station desktops for computerized provider order entry (CPOE) was found to be inefficient, increasing the work time from 98.1% to 328.6% of physician's time per working shift (weighted average of CPOE-oriented studies, 238.4%). Studies that conducted their evaluation process relatively soon after implementation of the EHR tended to demonstrate a reduction in documentation time in comparison to the increases observed with those that had a longer time period between implementation and the evaluation process. This review highlighted that a goal of decreased documentation time in an EHR project is not likely to be realized. It also identified how the selection of bedside or central station desktop EHRs may influence documentation time for the two main user groups, physicians and nurses.
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              Physician stress and burnout: the impact of health information technology

              To quantify how stress related to use of health information technology (HIT) predicts burnout among physicians.
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                Author and article information

                Journal
                J Am Med Inform Assoc
                J Am Med Inform Assoc
                jamia
                Journal of the American Medical Informatics Association : JAMIA
                Oxford University Press
                1067-5027
                1527-974X
                April 2020
                06 February 2020
                06 February 2020
                : 27
                : 4
                : 639-643
                Affiliations
                [1 ] Department of Medicine, American Medical Association , Chicago, Illinois, USA
                [2 ] Department of Medical Informatics and Clinical Epidemiology, Oregon Health Sciences University , Oregon, USA
                [3 ] Department of Medicine , Mathematica, Washington, DC, USA
                [4 ] Department of Family Medicine and Community Health, School of Medicine and Public Health, University of Wisconsin , Madison, Wisconsin, USA
                [5 ] Division of Hematology, Department of Medicine, Stanford University , Stanford, California, USA
                [6 ] Division of General Internal Medicine, Department of Medicine, Stanford University , Stanford, California, USA
                [7 ] Department of Biomedical Informatics, University of California , San Diego, San Diego, California, USA
                [8 ] Viterbi Family Department of Ophthalmology, Shiley Eye Institute, University of California , San Diego, San Diego, California, USA
                [9 ] Department of Family Medicine and Public Health, University of California , San Diego, San Diego, California, USA
                [10 ] Department of Medicine, Sutter Health, Walnut Creek , California, USA
                [11 ] Department of Biomedical Informatics, Vanderbilt University Medical Center , Nashville, Tennessee, USA
                [12 ] Department of Medicine, University of California , San Francisco, San Francisco, California, USA
                Author notes
                Corresponding Author: Christine A. Sinsky, MD, 330 N. Wabash Ave, Suite 39300, Chicago, IL 60611-5885, USA; christine.sinsky@ 123456ama-assn.org
                Author information
                http://orcid.org/0000-0003-2441-3320
                http://orcid.org/0000-0002-5271-7690
                Article
                ocz223
                10.1093/jamia/ocz223
                7075531
                32027360
                167936a0-1cdf-4779-bedb-73cba965dfbe
                © The Author(s) 2020. Published by Oxford University Press on behalf of the American Medical Informatics Association.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence ( http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contactjournals.permissions@oup.com

                History
                : 4 November 2019
                : 10 December 2019
                : 17 December 2019
                Page count
                Pages: 5
                Categories
                Perspective

                Bioinformatics & Computational biology
                metric,operational efficiency,ehr log data,time studies, burnout

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