Evidence map›Paper›PMID 31811860›Full record

ArticleAmerican journal of ophthalmology2020

Electronic Health Records in Ophthalmology: Source and Method of Documentation.

Bradley S Henriksen, Isaac H Goldstein, Adam Rule, Abigail E Huang, Haley Dusek, Austin Igelman, Michael F Chiang, Michelle R Hribar

Abstract read
In one paragraph

Article in American journal of ophthalmology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Article
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  7. Review
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  9. Article
  10. Article
  11. Methods for Large-Scale Quantitative Analysis of Scribe Impacts on Clinical Documentation.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2020
    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

8 authors.

Bradley S HenriksenDepartment of Ophthalmology, Casey Eye Institute, Oregon Health and Science University, Portland, Oregon, USA.
Isaac H GoldsteinDepartment of Ophthalmology, Casey Eye Institute, Oregon Health and Science University, Portland, Oregon, USA.
Adam RuleDepartment of Ophthalmology, Casey Eye Institute, Oregon Health and Science University, Portland, Oregon, USA; Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, Oregon, USA.
Abigail E HuangDepartment of Ophthalmology, Casey Eye Institute, Oregon Health and Science University, Portland, Oregon, USA.
Haley DusekDepartment of Ophthalmology, Casey Eye Institute, Oregon Health and Science University, Portland, Oregon, USA.
Austin IgelmanDepartment of Ophthalmology, Casey Eye Institute, Oregon Health and Science University, Portland, Oregon, USA.
Michael F ChiangDepartment of Ophthalmology, Casey Eye Institute, Oregon Health and Science University, Portland, Oregon, USA; Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, Oregon, USA.
Michelle R HribarDepartment of Ophthalmology, Casey Eye Institute, Oregon Health and Science University, Portland, Oregon, USA; Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, Oregon, USA. Electronic address: hribarm@ohsu.edu.

Funding

TRAINING PROGRAM IN HEALTH INFORMATICST15LM007088 · NLM · OREGON HEALTH & SCIENCE UNIVERSITY · PI WILLIAM R HERSH, Vishnu Mohan · 1992 to 2026
$23.4M
Proteomics CoreP30EY010572 · NEI · OREGON HEALTH & SCIENCE UNIVERSITY · PI TED S ACOTT · 1995 to 2026
$19.4M
Modeling and Optimization of Clinical Processes Using EHR DataR00LM012238 · NLM · OREGON HEALTH & SCIENCE UNIVERSITY · PI HRIBAR, MICHELLE · 2017 to 2019
$672k
NEI NIH HHS P30 EY010572NLM NIH HHS R00 LM012238NLM NIH HHS T15 LM007088
6 · The paper itself

Abstract

purposeThis study analyzed and quantified the sources of electronic health record (EHR) text documentation in ophthalmology progress notes.

designEHR documentation review and analysis.

methodsSetting: a single academic ophthalmology department. STUDY POPULATION: a cohort study conducted between November 1, 2016, and December 31, 2018, using secondary EHR data and a follow-up manual review of a random samples. The cohort study included 123,274 progress notes documented by 42 attending providers. These notes were for patients with the 5 most common primary International Statistical Classification of Diseases and Related Health Problems, version 10, parent codes for each provider. For the manual review, 120 notes from 8 providers were randomly sampled. Main outcome measurements were characters or number of words in each note categorized by attribution source, author type, and time of creation.

resultsImported text entries made up the majority of text in new and return patients, 2,978 characters (77%) and 3,612 characters (91%). Support staff members authored substantial portions of notes; 3,024 characters (68%) of new patient notes, 3,953 characters (83%) of return patient notes. Finally, providers completed large amounts of documentation after clinical visits: 135 words (35%) of new patient notes, 102 words (27%) of return patient notes.

conclusionsEHR documentation consists largely of imported text, is often authored by support staff, and is often written after the end of a visit. These findings raise questions about documentation accuracy and utility and may have implications for quality of care and patient-provider relationships.

Indexed as

Academic Medical CentersData AccuracyDocumentationElectronic Health RecordsHumansMedical RecordsOphthalmologyOregonOutpatientsPractice Patterns, Physicians'Retrospective Studies

Identifiers

PMID31811860
PMCPMC7073273

What OpenQuestion holds

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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.