Association of Primary Care Physicians’ Ambulatory Full-time Equivalencies With Time in the Electronic Health Record

Micek, Mark A.; Rule, Adam; Baltus, Jeffrey J; Arndt, Brian

doi:10.1001/jamanetworkopen.2023.20032

Cited by 2 publications

(1 citation statement)

References 5 publications

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“…Even then, the ability to take on additional EHR work is limited as physicians with lower clinical FTEs spend more time in the EHR per hour of clinic than their full-time peers. 18 When some PCPs reduce their aFTE with intent to reduce overall workload, their total EHR workload may remain high if their panel size does not proportionally decrease-which can result in unpaid work in compensation plans largely based on billable patient visits.…”

Section: Discussionmentioning

confidence: 99%

More Tethered to the EHR: EHR Workload Trends Among Academic Primary Care Physicians, 2019-2023

Arndt,

Micek,

Rule

et al. 2024

Ann Fam Med

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PURPOSEThe purpose of this study is to evaluate recent trends in primary care physician (PCP) electronic health record (EHR) workload.METHODS This longitudinal study observed the EHR use of 141 academic PCPs over 4 years (May 2019 to March 2023. Ambulatory full-time equivalency (aFTE), visit volume, and panel size were evaluated. Electronic health record time and inbox message volume were measured per 8 hours of scheduled clinic appointments. RESULTSFrom the pre-COVID-19 pandemic year (May 2019 to February 2020) to the most recent study year (April 2022 to March 2023), the average time PCPs spent in the EHR per 8 hours of scheduled clinic appointments increased (+28.4 minutes, 7.8%), as did time in orders (+23.1 minutes, 58.9%), inbox (+14.0 minutes, 24.4%), chart review (+7.2 minutes, 13.0%), notes (+2.9 minutes, 2.3%), outside scheduled hours on days with scheduled appointments (+6.4 minutes, 8.2%), and on unscheduled days (+13.6 minutes, 19.9%). Primary care physicians received more patient medical advice requests (+5.4 messages, 55.5%) and prescription messages (+2.3, 19.5%) per 8 hours of scheduled clinic appointments, but fewer patient calls (-2.8, -10.5%) and results messages (-0.3, -2.7%). While total time in the EHR continued to increase in the final study year (+7.7 minutes, 2.0%), inbox time decreased slightly from the year prior (-2.2 minutes, -3.0%). Primary care physicians' average aFTE decreased 5.2% from 0.66 to 0.63 over 4 years. CONCLUSIONS Primary care physicians' time in the EHR continues to grow. While PCPs' inbox time may be stabilizing, it is still substantially higher than pre-pandemic levels. It is imperative health systems develop strategies to change the EHR workload trajectory to minimize PCPs' occupational stress and mitigate unnecessary reductions in effective physician workforce resulting from the increased EHR burden.

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Section: Discussionmentioning

confidence: 99%

More Tethered to the EHR: EHR Workload Trends Among Academic Primary Care Physicians, 2019-2023

Arndt,

Micek,

Rule

et al. 2024

Ann Fam Med

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Improved accuracy and efficiency of primary care fall risk screening of older adults using a machine learning approach

Song,

Latham,

Liu

et al. 2024

J American Geriatrics Society

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BackgroundWhile many falls are preventable, they remain a leading cause of injury and death in older adults. Primary care clinics largely rely on screening questionnaires to identify people at risk of falls. Limitations of standard fall risk screening questionnaires include suboptimal accuracy, missing data, and non‐standard formats, which hinder early identification of risk and prevention of fall injury. We used machine learning methods to develop and evaluate electronic health record (EHR)‐based tools to identify older adults at risk of fall‐related injuries in a primary care population and compared this approach to standard fall screening questionnaires.MethodsUsing patient‐level clinical data from an integrated healthcare system consisting of 16‐member institutions, we conducted a case–control study to develop and evaluate prediction models for fall‐related injuries in older adults. Questionnaire‐derived prediction with three questions from a commonly used fall risk screening tool was evaluated. We then developed four temporal machine learning models using routinely available longitudinal EHR data to predict the future risk of fall injury. We also developed a fall injury‐prevention clinical decision support (CDS) implementation prototype to link preventative interventions to patient‐specific fall injury risk factors.ResultsQuestionnaire‐based risk screening achieved area under the receiver operating characteristic curve (AUC) up to 0.59 with 23% to 33% similarity for each pair of three fall injury screening questions. EHR‐based machine learning risk screening showed significantly improved performance (best AUROC = 0.76), with similar prediction performance between 6‐month and one‐year prediction models.ConclusionsThe current method of questionnaire‐based fall risk screening of older adults is suboptimal with redundant items, inadequate precision, and no linkage to prevention. A machine learning fall injury prediction method can accurately predict risk with superior sensitivity while freeing up clinical time for initiating personalized fall prevention interventions. The developed algorithm and data science pipeline can impact routine primary care fall prevention practice.

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Association of Primary Care Physicians’ Ambulatory Full-time Equivalencies With Time in the Electronic Health Record

Abstract: This cross-sectional study examines whether primary care physicians (PCPs) in the clinic part-time have reduced electronic health record (EHR) time commensurate with their clinical hours.

Cited by 2 publications

References 5 publications

More Tethered to the EHR: EHR Workload Trends Among Academic Primary Care Physicians, 2019-2023

More Tethered to the EHR: EHR Workload Trends Among Academic Primary Care Physicians, 2019-2023

Improved accuracy and efficiency of primary care fall risk screening of older adults using a machine learning approach

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