The Use of Artificial Intelligence Techniques in Nursing Data Systems: Scoping Review
Bibliographic record
Abstract
Introduction. Artificial intelligence and machine learning are technologies that assist in uncovering patterns in data that can inform clinical decision-making. The Registered Nurses’ Association of Ontario has used artificial intelligence techniques to assist in understanding impactful clinical practices and implementation strategies. This scoping review aimed to discover the adaptation and implementation of various artificial intelligence and machine learning techniques in various healthcare settings using different data systems that house nursing-related data. Methodology. In March 2022, a scoping review was conducted to search for peer-reviewed literature using the following terms: “nursing”, “artificial intelligence”, “data systems”, “statistics”, and “aggregated data”. Studies were excluded if they were not relevant to nursing, utilized qualitative or mixed-methods analyses, were literature review articles, and did not focus on artificial intelligence or the use of patient-level data. Results. A total of 2,627 articles were retrieved, with 1,518 articles remaining after de-duplication. Through title and abstract screening, 1,347 articles remained. Following the full-text screening, 13 studies remained. Artificial intelligence techniques used by healthcare data systems include regression, neural networks, classification, and graph-based methods, among others. Discussion. There is a gap in the application of artificial intelligence methods in data systems that evaluate the impact of implementing best practices in nursing. More data systems are needed that employ artificial intelligence techniques to support the evaluation of best practices in nursing and other health professions. Conclusions. Various artificial intelligence techniques in data systems housing nursing-related data were retrieved. However, more data systems and research are needed in this area.
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 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 | 0.032 | 0.141 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.026 | 0.031 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".