Evidence map›Paper›PMID 41726466›Full record

ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2024

Translating Nursing Data into Computational Metrics: An Evaluation Guideline for Inpatient Intravenous and Subcutaneous Insulin Management.

Varsha Varkhedi, Kenrick Cato, David Albers, Victoria L Tiase, Shalmali Joshi, Jennifer Thate, Kathryn Connell, William Hull, Amy Finnegan, Sarah C Rossetti

Abstract read
In one paragraph

Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2024. 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

10 authors.

Varsha VarkhediDepartment of Biomedical Informatics, Columbia University, New York, NY.
Kenrick CatoDepartment of Biobehavioral Health Sciences and Center for Health Outcomes and Policy Research, School of Nursing, University of Pennsylvania, Philadelphia, PA; Leonard Davis Institute of Health Economics and Palliative and Advanced Illness Research Center, University of Pennsylvania, Philadelphia, PA.
David AlbersBiomedical Informatics, University of Colorado, Boulder, CO.
Victoria L TiaseDepartment of Biomedical Informatics, University of Utah, Salt Lake City, UT.
Shalmali JoshiDepartment of Biomedical Informatics, Columbia University, New York, NY.
Jennifer ThateDepartment of Nursing , Siena College, Loudonville, NY.
Kathryn ConnellDepartment of Biobehavioral Health Sciences and Center for Health Outcomes and Policy Research, School of Nursing, University of Pennsylvania, Philadelphia, PA; Leonard Davis Institute of Health Economics and Palliative and Advanced Illness Research Center, University of Pennsylvania, Philadelphia, PA.
William HullCollege of Nursing, University of Utah, Salt Lake City, UT.
Amy FinneganIntraHealth International, Chapel Hill, NC.
Sarah C RossettiDepartment of Biomedical Informatics, Columbia University, New York, NY.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A challenge in utilizing electronic health record data for artificial intelligence models is contextualization, including understanding differences between missing data and missed care. Our team aims to develop knowledge graphs and computational models that account for these contexts, such as when data is missing (nurses being unable to document), but acceptable nursing care was delivered. We developed evaluation guidelines for intravenous and subcutaneous insulin management to establish a binary variable derived from EHR data representing minimally acceptable safe and quality nursing care for use in computational modeling. These guidelines were developed by our nurse informatics team based on best practices and validated by three nurse subject matter experts. The resulting evaluation guidelines are agnostic to institutional policies and focus on evaluating minimally acceptable safe and quality levels of care to inform inferences about missing data versus missed nursing care. Future work includes data-driven validations and expanding to other clinical scenarios.

Indexed as

Artificial IntelligenceElectronic Health RecordsHypoglycemic AgentsInsulinNursing InformaticsPractice Guidelines as TopicHumansInjections, SubcutaneousNursing CareHypoglycemic AgentsInsulin

Identifiers

PMID41726466
PMCPMC12919408

What Socratic holds

Textmetadata
Read underepoch 390

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 Socratic graph.