Evidence map›Paper›PMID 40712445›Full record

ArticleNursing outlook

Making nursing visible: AI-assisted standardization of electronic health record interventions using generative pre-trained transformer models and retrieval-augmented generation process.

Tamara G R Macieira, Ragnhildur I Bjarnadottir, Patricia de Oliveira Salgado, Aseem Baranwal, Alexander Semenov, Karen B Priola, Priscilla Pestana, Soluchukwu Okafor, Nathan Mena, Noelle Montoya and 4 more

Abstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Nursing outlook. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

14 authors.

Tamara G R MacieiraDepartment of Family, Community and Health System Science, College of Nursing, University of Florida, Gainesville, FL. Electronic address: tmacie2@ufl.edu.
Ragnhildur I BjarnadottirDepartment of Family, Community and Health System Science, College of Nursing, University of Florida, Gainesville, FL.
Patricia de Oliveira SalgadoDepartment of Medicine and Nursing, Federal University of Viçosa, Viçosa, MG, Brazil.
Aseem BaranwalDepartment of Computer and Information Science and Engineering, University of Florida, Gainesville, FL.
Alexander SemenovDepartment of Industrial and Systems Engineering, University of Florida, Gainesville, FL; Department of Industrial & Management Systems Engineering, University of South Florida, Tampa, FL.
Karen B PriolaDepartment of Family, Community and Health System Science, College of Nursing, University of Florida, Gainesville, FL.
Priscilla PestanaCollege of Nursing, University of Florida, Gainesville, FL.
Soluchukwu OkaforCollege of Nursing, University of Florida, Gainesville, FL.
Nathan MenaUniversity of Florida Health, Gainesville, FL.
Noelle MontoyaUniversity of Florida Health, Gainesville, FL.
Laura SargentUniversity of Florida Health, Gainesville, FL.
Ashley PresleyUniversity of Florida Health, Gainesville, FL.
Yingwei YaoDepartment of Biobehavioral Nursing Science, College of Nursing, University of Florida, Gainesville, FL.
Gail M KeenanDepartment of Family, Community and Health System Science, College of Nursing, University of Florida, Gainesville, FL.

Funding

Harmonizing and Integrating Nursing Data into Multidisciplinary Datasets to Evaluate Hospital Care and Readmissions of Older Adults with Alzheimer's Disease-Related DementiasR33AG072265 · NIA · UNIVERSITY OF FLORIDA · PI KEENAN, GAIL M, MACIEIRA, TAMARA G.R. · 2023 to 2025
$2.1M
Advancing Interdisciplinary Science of Aging through Identification of Iatrogenic Complications: The UF EHR Clinical Data Infrastructure for Enhanced Patient Safety among the Elderly (UF-ECLIPSE)R33AG062884 · NIA · UNIVERSITY OF FLORIDA · PI INGIBJARGARDOTTIR BJARNADOTTIR, RAGNHILDUR, LUCERO, ROBERT J · 2021 to 2023
$2.1M
NIA NIH HHS R33 AG062884NIA NIH HHS R33 AG072265
6 · The paper itself

Abstract

backgroundNurses provide essential care that significantly impacts patient outcomes. However, nursing care is rarely examined due to a lack of standardized nurse-generated data suitable for large-scale analysis. PURPOSE: To test the performance of retrieval-augmented generation (RAG) and generative pre-trained transformer (GPT) models in standardizing electronic health records nursing intervention terms.

methodsThe process involved: (a) manually mapping a subset of local terms to standardized terms to create a "gold standard" for comparison, (b) implementing and assessing the RAG pipeline with GPT models for standardization, and (c) incorporating the output into an AI-assisted manual mapping workflow. DISCUSSION: GPT models were comparable to human mappers but lacked sufficient accuracy for fully automated mapping.

conclusionAlthough GPT models are not yet reliable for full automation, they can reduce human workload and streamline the standardization process. This advancement represents an important step toward making nursing contributions visible through data-driven evaluation of nursing practices.

Indexed as

Artificial IntelligenceElectronic Health RecordsNursingAutomationHumansModels, NursingNursing InformaticsReproducibility of ResultsArtificial intelligenceElectronic health recordsNatural language processingNursingNursing informaticsStandardized nursing terminology

Identifiers

PMID40712445
PMCPMC13439567

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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.