ArticleJournal of clinical nursing2026
Integrating Artificial Intelligence in Nursing Practice With Decubitus Risk Prediction Alerts: A Pilot Process Evaluation.
Article in Journal of clinical nursing, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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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.
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Who cites it
3 citing papers in PubMed.
- Comment on: Integrating Artificial Intelligence in Nursing Practice With Decubitus Risk Prediction Alerts: A Pilot Process Evaluation.Journal of clinical nursing · 2026Article
- Artificial Intelligence Integration in Multidisciplinary Wound Management: A Scoping Review of Barriers and Facilitators in Clinical Workflows.Journal of multidisciplinary healthcare · 2026Review
- Smart Dressings for Pre-Ulcer Risk Surveillance and Closed-Loop Chronic Wound Management: Advances, Discrepancies, and Translational Horizons.International journal of nanomedicine · 2026Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimsTo evaluate the acceptability and feasibility among nurses of Decubitus Risk Prediction Alerts based on Artificial Intelligence (DRAAI), and to assess the feasibility of the implementation plan.
designA process evaluation of a pilot implementation study using mixed methods.
methodsAcceptability and feasibility of DRAAI among nurses from three general wards in a university hospital was assessed via questionnaire. The tailored implementation plan included thirteen strategies distributed over six domains, such as facilitation, continuous evaluation, and educational sessions. Adaptations, acceptability, and feasibility were recorded in field notes.
resultsFifty-five nurses completed the questionnaire and valued DRAAI's predictions, believing these could contribute to pressure ulcer (PU) prevention. Some initially faced challenges distinguishing between PU risk and PU detection. Most found it feasible to integrate DRAAI into their workflow. Adaptations included adding PU preventive measures to educational sessions and sharing frequently asked questions and answers. Overall, implementation efforts were feasible. DRAAI generated PU risk predictions for 428 unique admitted patients; 128 (30%) patients received at least one at-risk prediction. Regarding fidelity, nearly 80% (101/128) of at-risk predictions were followed by a nursing care plan.
conclusionOngoing involvement and clear communication were crucial for successfully integrating AI into nursing workflows. Although some nurses were concerned that DRAAI might miss at-risk patients, they continued to independently identify at-risk patients. IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE: Implementation of DRAAI served as a prompt for nurses to focus more on PU prevention. While DRAAI shows promise in improving PU prevention, future research is needed to evaluate its clinical impact. IMPACT: Addressed the challenge of identifying patients at risk for developing pressure ulcers. Demonstrated feasibility and acceptability of implementing AI in clinical practice. Highlighted the need for ongoing support and communication for successful implementation. PATIENT CONTRIBUTION: None. REPORTING
methodStandard for Reporting Implementation Studies (StaRI).
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Registered trials
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