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Integrating Artificial Intelligence in Nursing Practice With Decubitus Risk Prediction Alerts: A Pilot Process
Denise Spoon1, Annemarie de Vroed2, Steffen Greup3
1Department of Internal Medicine, Division of Nursing Science, Erasmus MC, Erasmus University Medical Centre, Rotterdam, the Netherlands.
Journal of Clinical Nursing
|January 20, 2026
Summary
Nurses found Decubitus Risk Prediction Alerts based on Artificial Intelligence (DRAAI) acceptable and feasible for preventing pressure ulcers. Integrating AI into nursing workflows requires ongoing support and clear communication for successful implementation.
Area of Science:
- Nursing Informatics
- Artificial Intelligence in Healthcare
- Patient Safety
Background:
- Pressure ulcers (PUs) pose a significant challenge in healthcare settings.
- Accurate risk prediction is crucial for effective PU prevention strategies.
- Integrating novel technologies like AI into clinical practice requires careful evaluation.
Purpose of the Study:
- To evaluate the acceptability and feasibility of Decubitus Risk Prediction Alerts based on Artificial Intelligence (DRAAI) among nurses.
- To assess the feasibility of the implementation plan for DRAAI in a hospital setting.
- To understand the impact of AI-driven alerts on nursing workflow and PU prevention.
Main Methods:
- A mixed-methods process evaluation of a pilot implementation study.
- Questionnaires assessed nurse acceptability and feasibility of DRAAI.
- Field notes recorded adaptations, acceptability, and feasibility during implementation.
- Analysis of patient data for alert generation and nursing care plan adherence.
Main Results:
- Fifty-five nurses found DRAAI predictions valuable for PU prevention, with most integrating it into their workflow.
- Adaptations included enhancing educational sessions and creating FAQs.
- DRAAI generated risk predictions for 30% of admitted patients, with nearly 80% receiving appropriate nursing care plans.
- Implementation efforts were deemed feasible overall.
Conclusions:
- Ongoing nurse involvement and clear communication are vital for successful AI integration into clinical workflows.
- DRAAI shows promise for improving PU prevention, though nurses continued to identify at-risk patients independently.
- Further research is needed to determine the clinical impact of DRAAI on PU prevention.
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