Evidence map›Paper›PMID 33661128›Full record

ArticleJournal of medical Internet research2021

Contribution of Free-Text Comments to the Burden of Documentation: Assessment and Analysis of Vital Sign Comments in Flowsheets.

Zhijun Yin, Yongtai Liu, Allison B McCoy, Bradley A Malin, Patricia R Sengstack

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Generative AI Demonstrated Difficulty Reasoning on Nursing Flowsheet Data.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024
    Article
  3. Review
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.

Zhijun YinDepartment of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States.ORCID 0000-0002-3075-1337
Yongtai LiuDepartment of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, United States.ORCID 0000-0002-0279-3644
Allison B McCoyDepartment of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States.ORCID 0000-0003-2292-9147
Bradley A MalinDepartment of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States.ORCID 0000-0003-3040-5175
Patricia R SengstackSchool of Nursing, Vanderbilt University, Nashville, TN, United States.ORCID 0000-0002-7807-2173

Funding

Vanderbilt Institute for Clinical and Translational Research (VICTR) -Identifying correlates of functional immunity in SARS-CoV-2 convalescent plasmaUL1TR002243 · NCATS · VANDERBILT UNIVERSITY MEDICAL CENTER · PI Paul A. Harris, Wesley H Self · 2017 to 2026
$130.7M
NCATS NIH HHS UL1 TR002243
6 · The paper itself

Abstract

backgroundDocumentation burden is a common problem with modern electronic health record (EHR) systems. To reduce this burden, various recording methods (eg, voice recorders or motion sensors) have been proposed. However, these solutions are in an early prototype phase and are unlikely to transition into practice in the near future. A more pragmatic alternative is to directly modify the implementation of the existing functionalities of an EHR system.

objectiveThis study aims to assess the nature of free-text comments entered into EHR flowsheets that supplement quantitative vital sign values and examine opportunities to simplify functionality and reduce documentation burden.

methodsWe evaluated 209,055 vital sign comments in flowsheets that were generated in the Epic EHR system at the Vanderbilt University Medical Center in 2018. We applied topic modeling, as well as the natural language processing Clinical Language Annotation, Modeling, and Processing software system, to extract generally discussed topics and detailed medical terms (expressed as probability distribution) to investigate the stories communicated in these comments.

resultsOur analysis showed that 63.33% (6053/9557) of the users who entered vital signs made at least one free-text comment in vital sign flowsheet entries. The user roles that were most likely to compose comments were registered nurse, technician, and licensed nurse. The most frequently identified topics were the notification of a result to health care providers (0.347), the context of a measurement (0.307), and an inability to obtain a vital sign (0.224). There were 4187 unique medical terms that were extracted from 46,029 (0.220) comments, including many symptom-related terms such as "pain," "upset," "dizziness," "coughing," "anxiety," "distress," and "fever" and drug-related terms such as "tylenol," "anesthesia," "cannula," "oxygen," "motrin," "rituxan," and "labetalol."

conclusionsConsidering that flowsheet comments are generally not displayed or automatically pulled into any clinical notes, our findings suggest that the flowsheet comment functionality can be simplified (eg, via structured response fields instead of a text input dialog) to reduce health care provider effort. Moreover, rich and clinically important medical terms such as medications and symptoms should be explicitly recorded in clinical notes for better visibility.

Indexed as

DocumentationElectronic Health RecordsAcademic Medical CentersHumansNatural Language ProcessingVital Signscontent analysisdocumentation burdenelectronic health systemflowsheetsfree textvital sign comments

Identifiers

PMID33661128
PMCPMC7974764

What OpenQuestion holds

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LicenceCC BY
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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.