Evidence map›Paper›PMID 38176094›Full record

ArticleInternational journal of medical informatics2024

Standardizing nursing data extracted from electronic health records for integration into a statewide clinical data research network.

Tamara G R Macieira, Yingwei Yao, Cassie Marcelle, Nathan Mena, Mikayla M Mino, Trieu M L Huynh, Caitlin Chiampou, Amanda L Garcia, Noelle Montoya, Laura Sargent and 1 more

Abstract read
In one paragraph

Article in International journal of medical informatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Observational
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
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

11 authors.

Tamara G R MacieiraDepartment of Family, Community and Health System Science, College of Nursing, University of Florida, PO Box 100197, Gainesville, FL 32610, United States. Electronic address: tmacie2@ufl.edu.
Yingwei YaoDepartment of Biobehavioral Nursing Science, College of Nursing, University of Florida, PO Box 100197, Gainesville, FL 32610, United States.
Cassie MarcelleUniversity of Florida Health Information Technology, 3011 SW Williston Rd, Gainesville, FL 32608, United States.
Nathan MenaUniversity of Florida Health, 1600 SW Archer Rd, Gainesville, FL 32608, United States.
Mikayla M MinoCollege of Nursing, University of Florida, PO Box 100197, Gainesville, FL 32610, United States.
Trieu M L HuynhCollege of Nursing, University of Florida, PO Box 100197, Gainesville, FL 32610, United States.
Caitlin ChiampouCollege of Nursing, University of Florida, PO Box 100197, Gainesville, FL 32610, United States.
Amanda L GarciaCollege of Nursing, University of Florida, PO Box 100197, Gainesville, FL 32610, United States.
Noelle MontoyaUniversity of Florida Health, 1600 SW Archer Rd, Gainesville, FL 32608, United States.
Laura SargentUniversity of Florida Health, 1600 SW Archer Rd, Gainesville, FL 32608, United States.
Gail M KeenanDepartment of Family, Community and Health System Science, College of Nursing, University of Florida, PO Box 100197, Gainesville, FL 32610, United States.

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
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 AG072265
6 · The paper itself

Abstract

backgroundCare plans documented by nurses in electronic health records (EHR) are a rich source of data to generate knowledge and measure the impact of nursing care. Unfortunately, there is a lack of integration of these data in clinical data research networks (CDRN) data trusts, due in large part to nursing care being documented with local vocabulary, resulting in non-standardized data. The absence of high-quality nursing care plan data in data trusts limits the investigation of interdisciplinary care aimed at improving patient outcomes.

objectiveTo map local nursing care plan terms for patients' problems and goals in the EHR of one large health system to the standardized nursing terminologies (SNTs), NANDA International (NANDA-I), and Nursing Outcomes Classification (NOC).

methodsWe extracted local problems and goals used by nurses to document care plans from two hospitals. After removing duplicates, the terms were independently mapped to NANDA-I and NOC by five mappers. Four nurses who regularly use the local vocabulary validated the mapping.

results83% of local problem terms were mapped to NANDA-I labels and 93% of local goal terms were mapped to NOC labels. The nurses agreed with 95% of the mapping. Local terms not mapped to labels were mapped to the domains or classes of the respective terminologies.

conclusionMapping local vocabularies used by nurses in EHRs to SNTs is a foundational step to making interoperable nursing data available for research and other secondary purposes in large data trusts. This study is the first phase of a larger project building, for the first time, a pipeline to standardize, harmonize, and integrate nursing care plan data from multiple Florida hospitals into the statewide CDRN OneFlorida+ Clinical Research Network data trust.

Indexed as

Electronic Health RecordsStandardized Nursing TerminologyHumansNursing RecordsVocabulary, ControlledData ManagementElectronic Health RecordsNursingNursing InformaticsStandardized Nursing Terminology

Identifiers

PMID38176094
PMCPMC11018263

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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.