ArticleJAMA network open2022
Prevalence and Sources of Duplicate Information in the Electronic Medical Record.
Article in JAMA network open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06792890 (A Randomized Controlled Trial of Two Ambient Artificial Intelligence Scribe Technologies to Improve Documentation Efficiency and Reduce Physician Burnout), which is not on this map. Cited by 35 papers, 1 of them a synthesis that pooled it.
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.
A Randomized Controlled Trial of Two Ambient Artificial Intelligence Scribe Technologies to Improve Documentation Efficiency and Reduce Physician Burnout
Who cites it
35 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Safety and usability of oncology information systems - a systematic review.Frontiers in oncology · 2024Pooled it
- The Growing Use of Copy-Paste and Its Impact on Creation and Propagation of Documentation Errors.CHEST pulmonary · 2026Article
- Measuring Depression Severity With Clinical Global Impression-Severity Scale Scores From Clinical Notes Using Large Language Models: Validation Study.JMIR formative research · 2026Article
- Liability Risks of Ambient Clinical Workflows With Artificial Intelligence for Clinicians, Hospitals, and Manufacturers.JCO oncology practice · 2026Article
- Collaborative and Cooperative Hospital "In-House" Medical Device Development and Implementation in the AI Age: The European Responsible AI Development (EURAID) Framework Compatible With European Values.Journal of medical Internet research · 2026Article
- Copy Tools in the Electronic Health Record: Perceptions, Implications, and Future Directions.JMIR medical informatics · 2025Article
- Identifying Bias at Scale in Clinical Notes Using Large Language Models.Mayo Clinic proceedings. Digital health · 2025Article
- Artificial intelligence and physician burnout: A productivity paradox.Learning health systems · 2025Article
- Article
- Implementation of screening and assessment tools for diagnosing opioid use disorder: A systematic review.Drug and alcohol dependence reports · 2025Review
- Exploring Provider Perceptions and Attitudes toward Copy-Paste and Copy-Forward in Clinical Documentation.Applied clinical informatics · 2025Article
- Comparing artificial intelligence- vs clinician-authored summaries of simulated primary care electronic health records.JAMIA open · 2025Article
- Train-Time and Test-Time Computation in Large Language Models for Error Detection and Correction in Electronic Medical Records: A Retrospective Study.Diagnostics (Basel, Switzerland) · 2025Article
- Making Progress on Progress Notes.Journal of graduate medical education · 2025Article
- Social determinants of health extraction from clinical notes across institutions using large language models.NPJ digital medicine · 2025Article
- Identifying Medication Review Topics to Be Documented in a Structured Form in Electronic Health Record Systems: Delphi Consensus Survey.Journal of medical Internet research · 2025Article
- Development of the TrendBurden Survey: Assessing Perceived Documentation Burden among Health Professionals in the United States.Applied clinical informatics · 2025Article
- LEVERAGING UNSTRUCTURED DATA IN ELECTRONIC HEALTH RECORDS TO DETECT ADVERSE EVENTS FROM PEDIATRIC DRUG USE - A SCOPING REVIEW.medRxiv : the preprint server for health sciences · 2025Article
- Privacy-, linguistic-, and information-preserving synthesis of clinical documentation through generative agents.Frontiers in artificial intelligence · 2025Article
- Copying in medical documentation: developing an evidence-based approach.Internal medicine journal · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Importance: Duplicated text is a well-documented hazard in electronic medical records (EMRs), leading to wasted clinician time, medical error, and burnout. This study hypothesizes that text duplication is prevalent and increases with time and EMR size and that duplicate information is shared across authors. Objective: To examine the prevalence and scope of duplication behavior in clinical notes from a large academic health system and the factors associated with duplication. Design, Setting, and Participants: This retrospective, cross-sectional analysis of note length and content duplication rates used a set of 10 adjacent word tokens (ie, a 10-gram) sliding-window approach to identify spans of text duplicated exactly from earlier notes in a patient's record for all inpatient and outpatient notes written within the University of Pennsylvania Health System from January 1, 2015, through December 31, 2020. Text duplicated from a different author vs text duplicated from the same author was quantified. Furthermore, novel text and duplicated text per author for various note types and author types, as well as per patient record by number of notes in the record, were quantified. Information scatter, another documentation hazard, was defined as the inverse of novel text per note, and the association between information duplication and information scatter was graphed. Data analysis was performed from January to March 2022. Main Outcomes and Measures: Total, novel, and duplicate text by note type and note author were determined, as were the mean intra-author and inter-author duplication per note by type and author. Results: There were a total of 104 456 653 notes for 1 960 689 unique patients consisting of 32 991 489 889 words; 50.1% of the total text in the record (16 523 851 210 words) was duplicated from prior text written about the same patient. The duplication fraction increased year-over-year, from 33.0% for notes written in 2015 to 54.2% for notes written in 2020. Of the text duplicated, 54.1% came from text written by the same author, whereas 45.9% was duplicated from a different author. Records with more notes had more total duplicate text, approaching 60%. Note types with high information scatter tended to have low information overload, and vice versa, suggesting a trade-off between these 2 hazards under the current documentation paradigm. Conclusions and Relevance: Duplicate text casts doubt on the veracity of all information in the medical record, making it difficult to find and verify information in day-to-day clinical work. The findings of this cross-sectional study suggest that text duplication is a systemic hazard, requiring systemic interventions to fix, and simple solutions such as banning copy-paste may have unintended consequences, such as worsening information scatter. The note paradigm should be further examined as a major cause of duplication and scatter, and alternative paradigms should be evaluated.
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