Evidence map›Paper›PMID 40046143›Full record

ArticleThe Brown journal of hospital medicine2023

Using Electronic Medical Record To Identify Changes In Inpatient Learner Experience.

Tingrui Zhao, Nadia Pasha, Poonam Sharma

Abstract read
In one paragraph

Article in The Brown journal of hospital medicine, 2023. 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

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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

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

3 authors.

Tingrui ZhaoDepartment of Medicine Duke University School of Medicine.
Nadia PashaDepartment of Medicine Duke University School of Medicine.
Poonam SharmaDepartment of Medicine Duke University School of Medicine.ORCID https://orcid.org/0000-0003-0668-3124

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: COVID-19 has been disruptive to inpatient medical care. While changes in prevalence of inpatient diagnoses have been published, little has been published about the impact on learner exposure to various diagnoses. Such changes are likely impacted by national and global trends, as well as local disease prevalence and policies. We hypothesized that the electronic medical record (EMR) may offer a tool to track learner experiences as they evolve in a time of rapid change. Methods: The top 20 most common diagnoses between April-July 2019 vs April-July 2020 on the resident inpatient internal medicine service were extracted from the Epic EMR and compared. This data was analyzed using a chi squared test and Bonferroni correction to identify statistically significant changes in case distribution in this time frame. Changes over this period were also compared for the nonteaching service. Results: The resident teaching service saw a statistically significant decrease in pulmonary cases (13% vs 2%, p < 0.005) from 2019 to 2020. Acute exacerbation of chronic obstructive pulmonary disease (COPD) and pneumonia, previously the 5th and 7th most common diagnoses in the 2019 period, dropped off the top twenty list for the teaching service. These 2 diagnoses remained on the non-teaching common encounter list, suggesting that COVID-19-related service/team assignments rather than disease prevalence may be a factor. There was also a statistically significant increase from 2019 to 2020 in cases pertaining to substance use intoxication on both teaching and non-teaching teams (0 vs 0.05 and 0 vs 0.02, respectively), mirroring trends in national data. Conclusion: Use of the EMR was able to rapidly identify changes in the most common diagnoses on the teaching service. This may be a tool to monitor the inpatient experience of learners over time, particularly in times of rapid local, regional, or global change.

Indexed as

COVID-19electronic medical recordEMRmedical education

Identifiers

PMID40046143
PMCPMC11878837

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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.