Evidence map›Paper›PMID 40378254›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA2025

Semi-automated pipeline to accelerate multi-site flowsheet alignment and concept mapping in electronic health records.

Hao Fan, Sarah C Rossetti, Jennifer Thate, Rosemary Mugoya, Albert M Lai, Po-Yin Yen

Abstract read
In one paragraph

Article in Journal of the American Medical Informatics Association : JAMIA, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Biomedical and health informatics Potpourri.Journal of the American Medical Informatics Association : JAMIA · 2025
    Article
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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

6 authors.

Hao FanInstitute for Informatics, Data Science and Biostatistics, Washington University School of Medicine, Saint Louis, MO 63110, United States.ORCID 0000-0001-6853-893X
Sarah C RossettiDepartment of Biomedical Informatics, Columbia University Medical Center, New York, NY 10032, United States.ORCID 0000-0003-2632-8867
Jennifer ThateDepartment of Biomedical Informatics, Columbia University Medical Center, New York, NY 10032, United States.ORCID 0000-0003-1750-8145
Rosemary MugoyaInstitute for Informatics, Data Science and Biostatistics, Washington University School of Medicine, Saint Louis, MO 63110, United States.
Albert M LaiInstitute for Informatics, Data Science and Biostatistics, Washington University School of Medicine, Saint Louis, MO 63110, United States.ORCID 0000-0002-9241-2656
Po-Yin YenInstitute for Informatics, Data Science and Biostatistics, Washington University School of Medicine, Saint Louis, MO 63110, United States.ORCID 0000-0002-7061-4185

Funding

AHRQ HHS R01 HS028454AHRQ HHS R01HS028454
6 · The paper itself

Abstract

objectivesHealth-care institutions customize electronic health record (EHR) configurations to reflect their unique workflows and patient care priorities. Ensuring EHR alignment across sites facilitates seamless information exchange. We developed a pipeline for EHR flowsheet alignment between health-care organizations. The pipeline is augmented by mapping flowsheet data fields to concepts in the Clinical Care Classification (CCC) nursing terminology. MATERIALS AND

methodsFlowsheet templates and measures from 2 study sites were transformed into template-measure (T-M) pairs. They were aligned through exact, lexical, or semantic matching. Lexical matches were assessed using Jaccard similarity and fuzzy matching methods. Semantic alignment was determined using cosine similarity between large language model-generated embeddings of T-M pairs and CCC concepts to rank and recommend the top n concepts in CCC. Concept mappings were evaluated based on whether concepts were mapped consistently within the CCC hierarchy.

resultsWe totally aligned 31 255 unique T-M pairs in acute care units and 27 012 T-M pairs in intensive care units from 2 study sites. When restricted to the top-ranked CCC concept (n = 1), we achieved a 63% flowsheet alignment rate with a 53% concept mapping rate. Expanding to the top 3 concepts (n = 3) improved alignment to 96.5% and concept mapping to 96%. DISCUSSION AND

conclusionElectronic health record data field alignment with concept mapping offers opportunities to standardize data elements presented in flowsheets across health-care sites. We demonstrated the feasibility of leveraging a semi-automated pipeline to streamline the EHR flowsheet alignment and accelerate the manual concept mapping process.

Indexed as

Electronic Health RecordsStandardized Nursing TerminologyHumansSemanticsconcept mappingdocumentationelectronic health recordshealth information interoperabilitylarge language models

Identifiers

PMID40378254
PMCPMC12202140

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.