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Background <p>The use of electronic health record (EHR) systems varies among primary care providers (PCPs). However, little is known about how numerous different EHR use behaviors, such as time spent and collaboration in the EHR, cluster together. Prior efforts to quantify characteristics of PCPs using EHRs have generally focused on single behaviors.</p> Objective <p>To identify patterns of EHR use among PCPs using a data-driven clustering approach.</p> Design <p>Cross-sectional study analyzing EHR data from the 2021 calendar year.</p> Participants <p>Primary care providers practicing in a large Massachusetts healthcare system.</p> Approach <p>PCPs were assigned to groups based on patterns of EHR use across 30 monthly variables from EHR data using a k-means clustering approach. We used Elbow, Silhouette, and Gap statistic methods to determine the number of clusters. Cluster characteristics were analyzed descriptively.</p> Key Results <p>In total, 163 PCPs were included; 103 (63%) PCPs were female, and 113 (69%) were White. Three distinct clusters of PCPs were identified, named based on the EHR characteristics that differed most across the clusters: (1) “High-engagement users”: 38% of PCPs; (2) “Low-engagement users”: 42%; and (3) “Moderate and selective users”: 20%.</p> Conclusions <p>This study identified three distinct patterns of EHR use among PCPs, characterized by different levels of engagement with EHR functionality and time spent in the EHR. Further studies are needed to explore how EHR-based interventions could be tailored to different provider workflow styles.</p>

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Behavioral Phenotypes in Electronic Health Record Use by Primary Care Providers: a Cluster Analysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月10日

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  • Katharina Tabea Jungo,
  • Niteesh K. Choudhry,
  • John A. Zambrano,
  • Thomas Isaac,
  • Nancy Haff,
  • Julie C. Lauffenburger

摘要

Background

The use of electronic health record (EHR) systems varies among primary care providers (PCPs). However, little is known about how numerous different EHR use behaviors, such as time spent and collaboration in the EHR, cluster together. Prior efforts to quantify characteristics of PCPs using EHRs have generally focused on single behaviors.

Objective

To identify patterns of EHR use among PCPs using a data-driven clustering approach.

Design

Cross-sectional study analyzing EHR data from the 2021 calendar year.

Participants

Primary care providers practicing in a large Massachusetts healthcare system.

Approach

PCPs were assigned to groups based on patterns of EHR use across 30 monthly variables from EHR data using a k-means clustering approach. We used Elbow, Silhouette, and Gap statistic methods to determine the number of clusters. Cluster characteristics were analyzed descriptively.

Key Results

In total, 163 PCPs were included; 103 (63%) PCPs were female, and 113 (69%) were White. Three distinct clusters of PCPs were identified, named based on the EHR characteristics that differed most across the clusters: (1) “High-engagement users”: 38% of PCPs; (2) “Low-engagement users”: 42%; and (3) “Moderate and selective users”: 20%.

Conclusions

This study identified three distinct patterns of EHR use among PCPs, characterized by different levels of engagement with EHR functionality and time spent in the EHR. Further studies are needed to explore how EHR-based interventions could be tailored to different provider workflow styles.