ArticleJAMIA open2024
Call me Dr Ishmael: trends in electronic health record notes available at emergency department visits and admissions.
Article in JAMIA open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 9 papers.
What it found
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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
9 citing papers in PubMed.
- Evaluating clinical AI summaries with large language models as judges.NPJ digital medicine · 2025Article
- The incremental value of unstructured data via natural language processing in machine learning-based COVID-19 mortality prediction: a comparative study.BMC medical informatics and decision making · 2025Article
- Dataset of anonymized discharge summaries of sepsis patients from a Brazilian tertiary hospital for NLP applications.Data in brief · 2025Article
- Development and validation of the provider documentation summarization quality instrument for large language models.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Automating Evaluation of AI Text Generation in Healthcare with a Large Language Model (LLM)-as-a-Judge.medRxiv : the preprint server for health sciences · 2025Article
- Leveraging artificial intelligence to reduce diagnostic errors in emergency medicine: Challenges, opportunities, and future directions.Academic emergency medicine : official journal of the Society for Academic Emergency Medicine · 2025Review
- Lessons learned on information retrieval in electronic health records: a comparison of embedding models and pooling strategies.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Current and future state of evaluation of large language models for medical summarization tasks.npj health systems · 2025Article
- Automating assignment of HIV+ patients into phenogroups from demography bound phenotype attack rates.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
Corrections and comments
- Erratum issued
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: Numerous studies have identified information overload as a key issue for electronic health records (EHRs). This study describes the amount of text data across all notes available to emergency physicians in the EHR, trended over the time since EHR establishment. Materials and Methods: We conducted a retrospective analysis of EHR data from a large healthcare system, examining the number of notes and a corresponding number of total words and total tokens across all notes available to physicians during patient encounters in the emergency department (ED). We assessed the change in these metrics over a 17-year period between 2006 and 2023. Results: The study cohort included 730 968 ED visits made by 293 559 unique patients and a total note count of 132 574 964. The median note count for all encounters in 2006 was 5 (IQR 1-16), accounting for 1735 (IQR 447-5521) words. By the last full year of the study period, 2022, the median number of notes had grown to 359 (IQR 84-943), representing 359 (IQR 84-943) words. Note and word counts were higher for admitted patients. Discussion: The volume of notes available for review by providers has increased by over 30-fold in the 17 years since the implementation of the EHR at a large health system. The task of reviewing these notes has become commensurately more difficult. These data point to the critical need for new strategies and tools for filtering, synthesizing, and summarizing information to achieve the promise of the medical record.
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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.