ArticleAmerican journal of ophthalmology2020
Electronic Health Records in Ophthalmology: Source and Method of Documentation.
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.
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.
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.
Who cites it
11 citing papers in PubMed.
- Benchmarking GPT-5, LLaMA, and Mistral for Clinical Named Entity Recognition in Ophthalmology Progress Notes.Translational vision science & technology · 2026Article
- Real-time automated billing for tobacco treatment: performance evaluation of the CigStopper machine learning framework.JAMIA open · 2026Article
- Visual Acuity: Assessment of Data Quality and Usability in an Electronic Health Record System.Ophthalmology science · 2023Article
- Using event logs to observe interactions with electronic health records: an updated scoping review shows increasing use of vendor-derived measures.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Prevalence and Sources of Duplicate Information in the Electronic Medical Record.JAMA network open · 2022Article
- Analysis on the completeness of case records of patients with penetrating ocular trauma following a multimodal intervention.Indian journal of ophthalmology · 2022Article
- From Data to Deployment: The Collaborative Community on Ophthalmic Imaging Roadmap for Artificial Intelligence in Age-Related Macular Degeneration.Ophthalmology · 2022Review
- Electronic health record note review in an outpatient specialty clinic: who is looking?JAMIA open · 2021Article
- Length and Redundancy of Outpatient Progress Notes Across a Decade at an Academic Medical Center.JAMA network open · 2021Article
- Big Data and Artificial Intelligence in Ophthalmology: Where Are We Now?Ophthalmology science · 2021Article
- Methods for Large-Scale Quantitative Analysis of Scribe Impacts on Clinical Documentation.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
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
Identifiers
What OpenQuestion holds
Registered trials
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.