Evidence map›Paper›PMID 36156143›Full record

ArticleJAMA network open2022

Prevalence and Sources of Duplicate Information in the Electronic Medical Record.

Jackson Steinkamp, Jacob J Kantrowitz, Subha Airan-Javia

Registry-linked trialAbstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
35citing papers in PubMed, 1 pooled it
–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.

NCT06792890 nacompletednot on this mapstarted 2024, after this paper: background citation

A Randomized Controlled Trial of Two Ambient Artificial Intelligence Scribe Technologies to Improve Documentation Efficiency and Reduce Physician Burnout

TypeinterventionalSponsorUniversity of California, Los AngelesRan2024 to 2025Enrolled238ConditionsPhysician Workflow, Artificial Intelligence (AI)ArmsUse Nabla AI Scribe tool provided, Use AI Scribe tool provided by Vendor B
3 · Its place in the literature

Who cites it

35 citing papers in PubMed, 1 synthesis or guideline pooled it.

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  14. Making Progress on Progress Notes.Journal of graduate medical education · 2025
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Jackson SteinkampDepartment of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia.
Jacob J KantrowitzRiver Records, LLC, Jamaica Plain, Massachusetts.
Subha Airan-JaviaDepartment of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

DocumentationElectronic Health RecordsCross-Sectional StudiesHumansPrevalenceRetrospective Studies

Identifiers

PMID36156143
PMCPMC9513649

What OpenQuestion holds

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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.