Quantifying Volume and Disparities in Electronic Workload

<h3>Context:</h3> Electronic health records (EHR) and patient portal systems have resulted in an unrestricted, undefined, and often uncompensated workload for Primary Care providers. Little information exists regarding the volume of work due to electronic portal messaging and if disparities exist among gender, race, age, and specialty of the provider. Objective: Quantify portal messaging patterns across the Rochester, MN Primary Care practices in 2022. Quantify distribution of uncompensated workload based on provider characteristics/demographics. Characterize the impact of uncompensated workload on burnout. <h3>Study Design and Analysis:</h3> Observational study (cross-sectional survey) and retrospective electronic chart analysis to quantify electronic workload volume and to establish a relationship to a provider’s demographics. <h3>Setting:</h3> Outpatient primary care practice at 4 clinic sites <h3>Population Studied:</h3> Primary care providers (physicians and advance practice providers) <h3>Intervention/Instrument:</h3> Cross-sectional survey and retrospective EHR analysis <h3>Outcome Measures:</h3> Retrospective EHR analysis and internal data provided data on volumes and types of messages, patient panel characteristics, time spent on EHR tasks. Provider survey data was linked to internal data, de-identified, and analyzed using general linear model and logistic regression model. <h3>Results:</h3> 192 surveys sent; 67% response rate. Analyzed demographic differences, patient panel characteristics, burden (objective and subjective), in-basket task workload, and total time outside scheduled work hours. Comparisons of means across race, gender, and age for the primary variables of interest showed little statistical differences. Differences were found in burnout rates with increased rates in those with reduced FTE. Women were 3.8 to 4.9 times (95% CI 1.5-15.1) more likely to have reduced FTE. <h3>Conclusions:</h3> Providers with reduced FTE do not have a statistically significant reduction in direct patient care, but remaining work time has more direct patient care than does that of those with reduced FTE. Those with reduced FTE have higher burnout rates. In-basket tasks per month most strongly correlates to size of patient panel.

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Publication Details

Journal
Healthcare informatics
Published
2024-11-20
DOI
https://doi.org/10.1370/afm.22.s1.6051
Primary Topic
Scheduling and Optimization Algorithms
Type
article
Field-Weighted Citation Impact
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article

Quantifying Volume and Disparities in Electronic Workload

Tien Pham Van, Elizabeth Cozine, Lorelle Ziegelbauer, Justin Poe et al.
Healthcare informatics
Scheduling and Optimization Algorithms
article

Quantifying Volume and Disparities in Electronic Workload

Tien Pham Van, Elizabeth Cozine, Lorelle Ziegelbauer, Justin Poe, Todd Huschka, Meghan Theofiles
article en

Abstract

<h3>Context:</h3> Electronic health records (EHR) and patient portal systems have resulted in an unrestricted, undefined, and often uncompensated workload for Primary Care providers. Little information exists regarding the volume of work due to electronic portal messaging and if disparities exist among gender, race, age, and specialty of the provider. Objective: Quantify portal messaging patterns across the Rochester, MN Primary Care practices in 2022. Quantify distribution of uncompensated workload based on provider characteristics/demographics. Characterize the impact of uncompensated workload on burnout. <h3>Study Design and Analysis:</h3> Observational study (cross-sectional survey) and retrospective electronic chart analysis to quantify electronic workload volume and to establish a relationship to a provider’s demographics. <h3>Setting:</h3> Outpatient primary care practice at 4 clinic sites <h3>Population Studied:</h3> Primary care providers (physicians and advance practice providers) <h3>Intervention/Instrument:</h3> Cross-sectional survey and retrospective EHR analysis <h3>Outcome Measures:</h3> Retrospective EHR analysis and internal data provided data on volumes and types of messages, patient panel characteristics, time spent on EHR tasks. Provider survey data was linked to internal data, de-identified, and analyzed using general linear model and logistic regression model. <h3>Results:</h3> 192 surveys sent; 67% response rate. Analyzed demographic differences, patient panel characteristics, burden (objective and subjective), in-basket task workload, and total time outside scheduled work hours. Comparisons of means across race, gender, and age for the primary variables of interest showed little statistical differences. Differences were found in burnout rates with increased rates in those with reduced FTE. Women were 3.8 to 4.9 times (95% CI 1.5-15.1) more likely to have reduced FTE. <h3>Conclusions:</h3> Providers with reduced FTE do not have a statistically significant reduction in direct patient care, but remaining work time has more direct patient care than does that of those with reduced FTE. Those with reduced FTE have higher burnout rates. In-basket tasks per month most strongly correlates to size of patient panel.

Healthcare informatics
Openalex Percentile: Top 69%
Scheduling and Optimization Algorithms
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