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Charting and Predicting Risk: Artificial Intelligence/Machine Learning Pilot Model for Hospital-Acquired Pressure
Shea Polancich1, Chris Hickman, Tracey Dick
1Author Affiliations: Department of Family, Community, and Health Systems (Drs Polancich and Bordelon), Department of Acute, Chronic, and Continuing Care, School of Nursing, University of Alabama at Birmingham (UAB) (Mr Hickman and Dr Dick), and Department of Health Services Administration, UAB School of Health Professions, Birmingham, Alabama (Drs Hall and Hearld).
Background:
Hospital-acquired pressure injuries (HAPIs) are a significant and global adverse event impacting an estimated 2.5 million patients per year, costing from $20 900 to $151 700 per pressure injury.
Purpose:
The purpose of this pilot study was 2-fold: (1) to evaluate the effectiveness of artificial intelligence and machine learning (AI/ML) models in predicting HAPIs, and (2) to compare that effectiveness with the predictive accuracy of traditional analytic methods.
Methods:
Secondary data analysis was performed for this pilot study. A training dataset was created and used for our exploratory evaluation and comparisons between AI/ML models and traditional analytic methods for pressure injury predictive accuracy.
Results:
While logistic regression provided a reasonable fit and interpretable coefficients, tree-based AI models performed notably better in predicting HAPIs.
Conclusions:
The use of AI/ML models appears to offer an increase in precision for the identification of patients at risk for developing HAPIs.