Evidence map›Paper›PMID 25954340›Full record

ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2014

Examining the use, contents, and quality of free-text tobacco use documentation in the Electronic Health Record.

Elizabeth S Chen, Elizabeth W Carter, Indra Neil Sarkar, Tamara J Winden, Genevieve B Melton

Abstract read
In one paragraph

Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 4 pooled it
–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

20 citing papers in PubMed, 4 syntheses or guidelines pooled it.

  1. Pooled it
  2. Electronic health record data quality assessment and tools: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2023
    Pooled it
  3. Digital Health Data Quality Issues: Systematic Review.Journal of medical Internet research · 2023
    Pooled it
  4. Pooled it
  5. Review
  6. Article
  7. Review
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Content and Quality of Free-Text Occupation Documentation in the Electronic Health Record.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2016
    Article
  17. Article
  18. Automated Extraction of Substance Use Information from Clinical Texts.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2015
    Article
  19. Towards the Standardized Documentation of E-Cigarette Use in the Electronic Health Record for Population Health Surveillance and Research.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2015
    Article
  20. Article
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.

Elizabeth S ChenCenter for Clinical & Translational Science, University of Vermont, Burlington, VT ; Medicine, University of Vermont, Burlington, VT.
Elizabeth W CarterCenter for Clinical & Translational Science, University of Vermont, Burlington, VT.
Indra Neil SarkarCenter for Clinical & Translational Science, University of Vermont, Burlington, VT ; Microbiology & Molecular Genetics, University of Vermont, Burlington, VT.
Tamara J WindenInstitute for Health Informatics, University of Minnesota, Minneapolis, MN ; Division of Applied Research, Allina Health, Minneapolis, MN.
Genevieve B MeltonInstitute for Health Informatics, University of Minnesota, Minneapolis, MN ; Surgery, University of Minnesota, Minneapolis, MN.

Funding

Leveraging the EHR to Collect and Analyze Social, Behavioral & Familial FactorsR01LM011364 · NLM · UNIVERSITY OF VERMONT & ST AGRIC COLLEGE · PI CHEN, ELIZABETH S., MELTON-MEAUX, GENEVIEVE B · 2012 to 2015
$1.4M
NLM NIH HHS R01 LM011364NLM NIH HHS R01LM011364
6 · The paper itself

Abstract

Recent initiatives have emphasized the potential role of Electronic Health Record (EHR) systems for improving tobacco use assessment and cessation. In support of these efforts, the goal of the present study was to examine tobacco use documentation in the EHR with an emphasis on free-text. Three coding schemes were developed and applied to analyze 525 tobacco use entries, including structured fields and a free-text comment field, from the social history module of an EHR system to characterize: (1) potential reasons for using free-text, (2) contents within the free-text, and (3) data quality issues. Free-text was most commonly used due to limitations for describing tobacco use amount (23.2%), frequency (26.9%), and start or quit dates (28.2%) as well as secondhand smoke exposure (17.9%) using a variety of words and phrases. The collective results provide insights for informing system enhancements, user training, natural language processing, and standards for tobacco use documentation.

Indexed as

Electronic Health RecordsTobacco UseClinical CodingDocumentationHumansNatural Language Processing

Identifiers

PMID25954340
PMCPMC4419995

What OpenQuestion 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 OpenQuestion graph.