Evidence map›Paper›PMID 41043477›Full record

ArticleApplied clinical informatics2025

EHR Use in Inpatient Physicians: Patterns and Predictors.

Gabrielle Safian, Kaiyu Tio, Kevin Tang, Shitij Arora, Sunit Jariwala

Abstract read
In one paragraph

Article in Applied clinical informatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Gabrielle Safian *Department of Medicine, Albert Einstein College of Medicine, Bronx, New York, United States.
Kaiyu Tio *Department of Medicine, Albert Einstein College of Medicine, Bronx, New York, United States.
Kevin TangDepartment of Medicine, Albert Einstein College of Medicine, Bronx, New York, United States.
Shitij AroraDepartment of Medicine, Albert Einstein College of Medicine, Bronx, New York, United States.
Sunit JariwalaDepartment of Medicine, Albert Einstein College of Medicine, Bronx, New York, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Electronic health record (EHR) systems have become integral to hospital-based care, with studies showing physicians spending significant time interfacing with these systems. While EHR interactions are necessary for patient care, understanding usage patterns can identify opportunities for system optimization and workflow improvement. Previous studies have focused on outpatient settings, making this study among the first to comprehensively analyze inpatient EHR interaction patterns.This study aims to characterize EHR utilization patterns among inpatient physicians and analyze how these patterns vary by physician characteristics, including gender, specialty, and years of experience. This analysis aims to identify opportunities for targeted EHR optimization and workflow enhancement strategies.We analyzed nine key EHR interaction metrics from Epic Signal across 1,787 inpatient physicians during February 2024. Metrics included time spent in various EHR activities, patient volume, secure message usage, and specific feature utilization. Multivariate regression models were then generated for each outcome metric.Female physicians spent more time per patient in the EHR (21.74 vs. 15.62 minutes,

Indexed as

Electronic Health RecordsInpatientsPhysiciansFemaleHumansMaleWorkflow

Identifiers

PMID41043477
PMCPMC12494415

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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 Socratic graph.