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Pressure Injury Prediction in Intensive Care Units Using Artificial Intelligence: A Scoping Review
José Alves1,2, Rita Azevedo1,2, Ana Marques1,3
1Center for Interdisciplinary Research in Health, Faculty of Health Sciences and Nursing, Universidade Católica Portuguesa, 4169-005 Porto, Portugal.
Insights
Artificial intelligence (AI) can predict pressure injuries in intensive care units. Machine learning models show promise in reducing patient harm and healthcare burdens.
Area of Science:
- Critical Care Medicine
- Health Informatics
- Artificial Intelligence in Healthcare
Background:
- Pressure injuries are a significant healthcare challenge, especially in intensive care units (ICUs).
- Existing risk assessment tools for pressure injuries have limitations.
- Artificial intelligence (AI) offers a novel approach for pressure injury prediction in critical care.
Purpose of the Study:
- To conduct a scoping review of AI technologies for pressure injury prediction in ICU patients.
- To identify knowledge gaps and guide future research in this area.
Main Methods:
- Adherence to the Joanna Briggs Institute's methodology for scoping reviews.
- Prospective registration of the study protocol on the Open Science Framework platform.
Main Results:
- The review included 14 studies, predominantly using machine learning models trained on electronic health records (EHRs).
- Models utilized between 6 and 86 variables for training.
- Clinical deployment was reported in only two studies, showing reduced nursing workload, fewer hospital-acquired pressure injuries, and shorter ICU stays.
Conclusions:
- AI technologies offer a dynamic and innovative method for effective and timely pressure injury risk identification and prediction.
- This review synthesizes current literature and provides direction for future research and development in AI for pressure injury prevention.
Abstract:
Background/Objetives: Pressure injuries pose a significant challenge in healthcare, adversely impacting individuals' quality of life and healthcare systems, particularly in intensive care units. The effective identification of at-risk individuals is crucial, but traditional scales have limitations, prompting the development of new tools. Artificial intelligence offers a promising approach to identifying and preventing pressure injuries in critical care settings. This review aimed to assess the extent of the literature regarding the use of artificial intelligence technologies in the prediction of pressure injuries in critically ill patients in intensive care units to identify gaps in current knowledge and direct future research. Methods: The review followed the Joanna Briggs Institute's methodology for scoping reviews, and the study protocol was prospectively registered on the Open Science Framework platform. Results: This review included 14 studies, primarily highlighting the use of machine learning models trained on electronic health records data for predicting pressure injuries. Between 6 and 86 variables were used to train these models. Only two studies reported the clinical deployment of these models, reporting results such as reduced nursing workload, decreased prevalence of hospital-acquired pressure injuries, and decreased intensive care unit length of stay. Conclusions: Artificial intelligence technologies present themselves as a dynamic and innovative approach, with the ability to identify risk factors and predict pressure injuries effectively and promptly. This review synthesizes information about the use of these technologies and guides future directions and motivations.
