โก Quick Summary
This pilot study evaluated the acceptability and feasibility of Decubitus Risk Prediction Alerts based on Artificial Intelligence (DRAAI) among nurses in a university hospital. The findings suggest that DRAAI can effectively assist in pressure ulcer prevention, with nearly 80% adherence to nursing care plans following at-risk predictions.
๐ Key Details
- ๐ Participants: 55 nurses from three general wards
- ๐งฉ Implementation Strategies: 13 strategies across six domains
- โ๏ธ Technology: DRAAI for predicting pressure ulcer risk
- ๐ Predictions: 428 unique patients, with 30% receiving at least one at-risk prediction
๐ Key Takeaways
- ๐ค AI Integration: DRAAI shows promise in enhancing nursing workflows for pressure ulcer prevention.
- ๐ก Nurse Feedback: Nurses valued the predictions but faced initial challenges in distinguishing risk from detection.
- ๐ Feasibility: Most nurses found it feasible to integrate DRAAI into their daily routines.
- ๐ Adaptations: Educational sessions were adjusted to include PU preventive measures based on nurse feedback.
- ๐ Care Plans: 80% of at-risk predictions were followed by appropriate nursing care plans.
- ๐ฃ๏ธ Communication: Ongoing support and clear communication were essential for successful implementation.
- ๐ Future Research: Further studies are needed to evaluate the clinical impact of DRAAI on patient outcomes.
๐ Background
Pressure ulcers (PUs) are a significant concern in healthcare settings, often leading to serious complications for patients. The integration of Artificial Intelligence in nursing practice presents an innovative approach to enhance patient care. By utilizing AI for risk prediction, healthcare professionals can proactively address PU prevention, ultimately improving patient outcomes and reducing healthcare costs.
๐๏ธ Study
Conducted in a university hospital, this pilot study aimed to assess the acceptability and feasibility of DRAAI among nurses. The study employed a mixed-methods approach, utilizing questionnaires to gather feedback from 55 nurses across three general wards. The tailored implementation plan included various strategies to facilitate the integration of DRAAI into nursing workflows.
๐ Results
The study revealed that DRAAI generated PU risk predictions for 428 unique patients, with 30% of these patients receiving at least one at-risk prediction. Notably, nearly 80% of the at-risk predictions were followed by a nursing care plan, indicating a high level of fidelity in the implementation process. Nurses expressed a strong belief that DRAAI could contribute significantly to PU prevention efforts.
๐ Impact and Implications
The integration of DRAAI into nursing practice addresses the critical challenge of identifying patients at risk for developing pressure ulcers. This pilot study demonstrates the feasibility and acceptability of implementing AI in clinical settings, highlighting the potential for improved patient care. As nurses become more engaged with AI technologies, the focus on PU prevention is likely to enhance overall patient outcomes.
๐ฎ Conclusion
This pilot study underscores the importance of ongoing support and communication in successfully integrating AI into nursing workflows. While DRAAI shows promise in improving PU prevention, further research is essential to evaluate its long-term clinical impact. The future of nursing practice may very well be shaped by the continued integration of AI technologies, paving the way for enhanced patient care.
๐ฌ Your comments
What are your thoughts on the integration of AI in nursing practice? Do you believe technologies like DRAAI can significantly improve patient care? ๐ฌ Share your insights in the comments below or connect with us on social media:
Integrating Artificial Intelligence in Nursing Practice With Decubitus Risk Prediction Alerts: A Pilot Process Evaluation.
Abstract
AIMS: To 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.
DESIGN: A process evaluation of a pilot implementation study using mixed methods.
METHODS: Acceptability 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.
RESULTS: Fifty-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.
CONCLUSION: Ongoing 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 METHOD: Standard for Reporting Implementation Studies (StaRI).
Author: [‘Spoon D’, ‘de Vroed A’, ‘Greup S’, ‘van Dijk M’, ‘Ista E’]
Journal: J Clin Nurs
Citation: Spoon D, et al. Integrating Artificial Intelligence in Nursing Practice With Decubitus Risk Prediction Alerts: A Pilot Process Evaluation. Integrating Artificial Intelligence in Nursing Practice With Decubitus Risk Prediction Alerts: A Pilot Process Evaluation. 2026; (unknown volume):(unknown pages). doi: 10.1111/jocn.70212