Exploring intensive care unit nurses' acceptance of clinical decision support systems and use of volumetric pump data: A qualitative description study
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".