Evidence map›Paper›PMID 40417573›Full record

ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2024

EHR Documentation Frequency Changes Across the COVID-19 Pandemic.

Hao Fan, Sarah Rossetti, Rosemary Mugoya, Haomiao Jia, Jennifer Thate, Amy Finnegan, Albert M Lai, Kenrick Cato, Po-Yin Yen

Abstract read
In one paragraph

Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2024. 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

9 authors.

Hao FanInstitute for Informatics and Data Science, Washington University School of Medicine, St. Louis, MO.
Sarah RossettiDepartment of Biomedical Informatics, Columbia University Medical Center, New York, NY.
Rosemary MugoyaGoldfarb School of Nursing, Barns-Jewish College, BJC HealthCare, St. Louis, MO.
Haomiao JiaDepartment of Biomedical Informatics, Columbia University Medical Center, New York, NY.
Jennifer ThateDepartment of Biomedical Informatics, Columbia University Medical Center, New York, NY.
Amy FinneganDepartment of Biomedical Informatics, Columbia University Medical Center, New York, NY.
Albert M LaiInstitute for Informatics and Data Science, Washington University School of Medicine, St. Louis, MO.
Kenrick CatoSchool of Nursing, University of Pennsylvania, Philadelphia, PA.
Po-Yin YenInstitute for Informatics and Data Science, Washington University School of Medicine, St. Louis, MO.

Funding

AHRQ HHS R01 HS028454
6 · The paper itself

Abstract

Documentation is crucial for patient care. However, the increased documentation burden raises questions about its clinical value. The COVID-19 pandemic provided an opportunity to explore nurses' documentation patterns due to increased patient care demands and the implementation of documentation relaxation policy. We conducted a trend analysis to examine documentation frequency over time, including phases during the pandemic and the implementation of the Surge Documentation, a documentation relaxation policy at a Midwest academic medical center. We analyzed the trend changes using segmented regression and mixed-effect Poisson regression. We found that documentation frequency increased in response to the pandemic due to the heightened demand for patient care and significantly decreased with the implementation of Surge documentation. The reduction was particularly noticeable in flowsheets unrelated to patient acuity. The study highlighted nurses' critical thinking in prioritizing documentation based on patient care. Future policies should support nurses'autonomy in documentation without imposing excessive requirements.

Indexed as

COVID-19DocumentationElectronic Health RecordsAcademic Medical CentersHumansPandemicsSARS-CoV-2Data MiningDocumentation BurdenNursing InformaticsTrend Analysis

Identifiers

PMID40417573
PMCPMC12099388

What OpenQuestion holds

Textmetadata
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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 OpenQuestion graph.