Evidence map›Paper›PMID 30815175›Full record

ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2018

Clinical Documentation in Electronic Health Record Systems: Analysis of Similarity in Progress Notes from Consecutive Outpatient Ophthalmology Encounters.

Abigail E Huang, Michelle R Hribar, Isaac H Goldstein, Brad Henriksen, Wei-Chun Lin, Michael F Chiang

Abstract read
In one paragraph

Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. Clinical Documentation as End-User Programming.Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference · 2020
    Article
  10. Article
  11. Forced Inefficiencies of the Electronic Health Record.Journal of general internal medicine · 2019
    Article
  12. 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

6 authors.

Abigail E HuangOphthalmology, VA Portland, OR.
Michelle R HribarMedical Informatics & Clinical Epidemiology.
Isaac H GoldsteinOphthalmology, OHSU, Portland, OR.
Brad HenriksenOphthalmology, OHSU, Portland, OR.
Wei-Chun LinMedical Informatics & Clinical Epidemiology.
Michael F ChiangMedical Informatics & Clinical Epidemiology.

Funding

Modeling and Optimization of Clinical Processes Using EHR DataR00LM012238 · NLM · OREGON HEALTH & SCIENCE UNIVERSITY · PI HRIBAR, MICHELLE · 2017 to 2019
$672k
NLM NIH HHS R00 LM012238
6 · The paper itself

Abstract

Content importing technology enables duplication of large amounts of clinical text in electronic health record (EHR) progress notes. It can be difficult to find key sections such as Assessment and Plan in the resulting note. To quantify the extent of text length and duplication, we analyzed average ophthalmology note length and calculated novelty of each major note section (Subjective, Objective, Assessment, Plan, Other). We performed a retrospective chart review of consecutive note pairs and found that the average encounter note was 1182 ± 374 words long and less than a quarter of words changed between visits. The Plan note section had the highest percentage of change, and both the Assessment and Plan sections comprised a small fraction of the full note. Analysis of progress notes by section and unique content helps describe physician documentation activity and inform best practices and EHR design recommendations.

Indexed as

Electronic Health RecordsOphthalmologyAmbulatory CareDocumentationHumansRetrospective Studies

Identifiers

PMID30815175
PMCPMC6371381

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.