Evidence map›Paper›PMID 39491050›Full record

ArticleThe Journal of perinatal & neonatal nursing

Creating Perinatal Nursing Care Plans Using ChatGPT: A Pathway to Improve Nursing Care Plans and Reduce Documentation Burden.

Lisa G Johnson, Olatunde O Madandola, Fabiana Cristina Dos Santos, Karen J B Priola, Yingwei Yao, Tamara G R Macieira, Gail M Keenan

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In one paragraph

Article in The Journal of perinatal & neonatal nursing. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing 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.

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

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

  1. Pooled it
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  5. Review
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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

7 authors.

Lisa G JohnsonAuthor Affiliations: College of Nursing, University of Florida College of Nursing, Gainesville, Florida (Ms Johnson, Mr Madandola, Ms Priola, and Drs Yao, Macieira, and Keenan); and School of Nursing, Columbia University, New York, New York (Dr Dos Santos).
Olatunde O Madandola
Fabiana Cristina Dos Santos
Karen J B Priola
Yingwei Yao
Tamara G R Macieira
Gail M Keenan

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
Tailored Clinical Decision Support Formats Designed to Improve Palliative Care for Cancer and Chronically Ill Patients: A Pre-Clinical TestR01NR018416 · NINR · UNIVERSITY OF FLORIDA · PI DUNN LOPEZ, KAREN, KEENAN, GAIL M · 2019 to 2021
$1.7M
Harmonizing and Integrating Nursing Data into Multidisciplinary Datasets to Evaluate Hospital Care and Readmissions of Older Adults with Alzheimer’s Disease and Alzheimer’s Disease-Related DementiasR21AG072265 · NIA · UNIVERSITY OF FLORIDA · PI KEENAN, GAIL M, MACIEIRA, TAMARA G.R. · 2021 to 2022
$417k
NIA NIH HHS R21 AG072265NIA NIH HHS R33 AG072265NINR NIH HHS R01 NR018416
6 · The paper itself

Abstract

backgroundExtensive time spent on documentation in electronic health records (EHRs) impedes patient care and contributes to nurse burnout. Artificial intelligence-based clinical decision support tools within the EHR, such as ChatGPT, can provide care plan recommendations to the perinatal nurse. The lack of explicit methodologies for effectively integrating ChatGPT led to our initiative to build and demonstrate our ChatGPT-4 prompt to support nurse care planning.

methodsWe employed our process model, previously tested with 22 diverse medical-surgical patient scenarios, to generate a tailored prompt for ChatGPT-4 to produce care plan suggestions for an exemplar patient presenting with preterm labor and gestational diabetes. A comparative analysis was conducted by evaluating the output against a "nurse-generated care plan" developed by our team of nurses on content alignment, accuracy of standardized nursing terminology, and prioritization of care.

resultsChatGPT-4 delivered suggestions for nursing diagnoses, interventions, and outcomes comparable to the "nurse-generated care plan." It accurately identified major care areas, avoided irrelevant or unnecessary recommendations, and identified top priority care. Of the 24 labels generated by ChatGPT-4, 16 correctly utilized standardized nursing terminology.

conclusionThis demonstration of the use of our ChatGPT-4 prompt illustrates the potential of leveraging a large language model to assist perinatal nurses in creating care plans. The next steps are improving the accuracy of ChatGPT-4-generated standardized nursing terminology and integrating our prompt into EHRs. This work supports our broader goal of enhancing patient outcomes while mitigating the burden of documentation that contributes to nurse burnout.

Indexed as

Decision Support Systems, ClinicalDocumentationNeonatal NursingPatient Care PlanningPerinatal CareBurnout, ProfessionalElectronic Health RecordsFemaleGenerative Artificial IntelligenceHumansPregnancy

Identifiers

PMID39491050
PMCPMC11781987

What OpenQuestion holds

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Registered trials

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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.