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Understanding physicians' work via text analytics on EHR inbox messages.

Célia Escribe, Stephanie A Eisenstat, Walter J O'Donnell

¹Sloan School of Management, Massachusetts Institute of Technology, Operations Research Center, 100 Main St, E62-416, Cambridge, MA 02142.

The American Journal of Managed Care

|January 20, 2022

Summary

This summary is machine-generated.

Primary care physicians spend over half their electronic health record inbox time on medical issues and nearly a third on administrative tasks. Advanced text analytics reveals workload drivers, aiding workflow redesign for better care and satisfaction.

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Area of Science:

Health Informatics
Medical Informatics
Clinical Workflow Analysis

Background:

Electronic Health Records (EHRs) generate substantial physician inbox messages.
Understanding the drivers of this workload is crucial for optimizing primary care.
Previous methods lacked the granularity to detail EHR inbox task composition.

Purpose of the Study:

To develop and apply a text analytics methodology for analyzing primary care physicians' (PCPs') EHR inbox workload.
To identify and quantify the specific work themes contributing to PCP inbox management.
To assess the variability of workload drivers across physicians and practices.

Main Methods:

Utilized 1 year (2018) of EHR inbox messages from the Epic system for 184 PCPs across 18 practices.
Employed an advanced text analytics latent Dirichlet allocation model to categorize message content.
Trained the model on physicians' inbox message texts to identify work themes and workload distribution.

Main Results:

Identified 30 distinct work themes within EHR inbox messages, categorized as medical and administrative.
Found that 50.8% of messages concerned medical issues and 34.1% administrative matters.
Specific tasks included ambiguous diagnosis (13.6%), condition management (13.2%), paperwork (9.5%), and scheduling (17.6%), with significant inter-physician variability.

Conclusions:

Advanced text analytics offers a reliable, data-driven method to analyze EHR inbox workload with high detail.
This methodology can inform workflow redesign to reduce PCP burden and enhance care quality.
Findings support improvements in cost, quality of care, and staff work satisfaction.