Evidence map›Paper›PMID 40640601›Full record

ArticleJournal of general internal medicine2025

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

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

Abstract read
In one paragraph

Article in Journal of general internal medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

6 authors.

Katharina Tabea JungoCenter for Healthcare Delivery Sciences (C4HDS), Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA. katharina.jungo@unibe.ch.ORCID 0000-0002-1782-1345
Niteesh K ChoudhryCenter for Healthcare Delivery Sciences (C4HDS), Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
John A ZambranoDepartment of Internal Medicine, Atrius Health, Newton, MA, USA.
Thomas IsaacDepartment of Internal Medicine, Atrius Health, Newton, MA, USA.
Nancy HaffCenter for Healthcare Delivery Sciences (C4HDS), Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Julie C LauffenburgerCenter for Healthcare Delivery Sciences (C4HDS), Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.

Funding

Optimizing electronic health record prompts with behavioral economics to improve prescribing for older adultsR33AG057388 · NIA · BRIGHAM AND WOMEN'S HOSPITAL · PI CHOUDHRY, NITEESH K, LAUFFENBURGER, JULIE CHRISTINE · 2019 to 2021
$2.2M
NIA NIH HHS R33 AG057388NIA NIH HHS R33AG057388
6 · The paper itself

Abstract

backgroundThe 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.

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

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

participantsPrimary 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

resultsIn 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%.

conclusionsThis 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.

Indexed as

Electronic Health RecordsPhysicians, Primary CarePrimary Health CareAdultCluster AnalysisCross-Sectional StudiesFemaleHumansMaleMassachusettsMiddle AgedPhenotypeCluster analysisEHR dataEHR use patternElectronic health recordsK-means clusteringPrimary care provider

Identifiers

PMID40640601
PMCPMC12612465

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.