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Record W4408026068 · doi:10.1111/nicc.13274

Exploring intensive care unit nurses' acceptance of clinical decision support systems and use of volumetric pump data: A qualitative description study

2025· article· en· W4408026068 on OpenAlexafffundabout
Christian Vincelette, François Martin Carrier, Charles Bilodeau, Michaël Chassé

Bibliographic record

VenueNursing in Critical Care · 2025
Typearticle
Languageen
FieldMedicine
TopicHealthcare Technology and Patient Monitoring
Canadian institutionsUniversité de SherbrookeUniversité de MontréalCentre Hospitalier de l’Université de Montréal
FundersCanadian Institutes of Health Research
KeywordsIntensive care unitQualitative researchDecision support systemClinical decision support systemNursingUnit (ring theory)MedicinePsychologyIntensive care medicineComputer scienceData miningSociology

Abstract

fetched live from OpenAlex

BACKGROUND: Intensive care units are well positioned for the rapid development of data-driven clinical decision support systems. However, clinical decision support systems using volumetric pump data are uncommon. This may be explained by the complexity of this data source as well as our limited understanding of the acceptability of clinical decision support systems and volumetric pump data use from nurses' perspectives. AIM: To describe intensive care unit nurses' perceptions regarding (1) the acceptability of developing and implementing novel intensive care technologies (i.e. clinical decision support systems) and (2) the acceptability of using infusion pump data to inquire about intensive care practices and improve the quality of care. STUDY DESIGN: A qualitative description study was performed. Semi-structured interviews were conducted between January and March 2024 and involved 10 intensive care nurses from the province of Quebec (Canada). RESULTS: Nurses generally perceived the development and implementation of novel technologies, and the use of pump data, as acceptable. However, the discrepancy between the delays in care computerization and the rapid development of novel technologies with advanced algorithmic capabilities, coupled with nurses' doubts and limited comprehension of data-driven clinical decision support systems, influenced their perspectives. Nurses' appraisal that infusion logs can enhance clinical practices and that logs should align with their documentation motivated their perception that it is acceptable to use this data source. CONCLUSIONS: Overall, novel technologies as well as volumetric pump data use were perceived as acceptable. Leveraging novel data processing and computation techniques could lead to the development of more dynamic clinical decision support systems that utilize infusion logs, further improving care delivery. RELEVANCE TO CLINICAL PRACTICE: For clinical decision support systems to be useful for intensive care nurses, alarms must be seamlessly integrated into their workflows. Involving nurses in the technological development process may help ensure the usability of these technologies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.019
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.605
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.661
GPT teacher head0.593
Teacher spread0.068 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2025
Admission routes3
Has abstractyes

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