Abstract
BackgroundHospital-acquired pressure injuries (PIs) induce significant patient suffering, inflate healthcare costs, and increase clinical co-morbidities. PIs are mostly due to bed-immobility, sensory impairment, bed positioning, and length of hospital stay. In this study, we use electronic health records and administrative data to examine the contributing factors to PI development using artificial intelligence (AI).MethodsWe used advanced data science techniques to first preprocess the data and then train machine learning classifiers to predict the probability of developing PIs. The AI training was based on large, incongruent, incomplete, heterogeneous, and time-varying data of hospitalized patients. Both model-based statistical methods and model-free AI strategies were used to forecast PI outcomes and determine the salient features that are highly predictive of the outcomes.ResultsOur findings reveal that PI prediction by model-free techniques outperform model-based forecasts. The performance of all AI methods is improved by rebalancing the training data and by including the Braden in the model learning phase. Compared to neural networks and linear modeling, with and without rebalancing or using Braden scores, Random forest consistently generated the optimal PI forecasts.ConclusionsAI techniques show promise to automatically identify patients at risk for hospital acquired PIs in different surgical services. Our PI prediction model provide a first generation of AI guidance to prescreen patients at risk for developing PIs.Clinical impactThis study provides a foundation for designing, implementing, and assessing novel interventions addressing specific healthcare needs. Specifically, this approach allows examining the impact of various dynamic, personalized, and clinical-environment effects on PI prevention for hospital patients receiving care from various surgical services.
Highlights
Hospital-acquired pressure injuries (PIs) induce significant patient suffering, inflate healthcare costs, and increase clinical co-morbidities
Data pertaining to the intraoperative phase were collected for each procedure that occurred during the specific hospitalization
The American Society of Anesthesiologists (ASA) score was used as a measure of severity of illness and the length and type of the procedure were recorded for each operation
Summary
Hospital-acquired pressure injuries (PIs) induce significant patient suffering, inflate healthcare costs, and increase clinical co-morbidities. Pressure injuries (PIs) continue to negatively impact clinical practice and increase patient suffering. Research reports in the literature about the incidence and prevalence of PIs are numerous. Three recent systematic reviews illustrate the difficulty of quantifying the incidence and prevalence of the problem. Bufone et al [1] conducted a systematic review of perioperative PIs. Of the eleven articles that met their inclusion criteria, the incidence range was between 1.3% and 54.8%. The authors note that likely contributors to their heterogeneous findings were that the studies varied on the PI stages that were included (e.g., Stage I, I & II, III & IV), assessment tool used (Braden, Norton, RAPS or not reported), and type of surgery (orthopedics, cardiac, ENT, others). Recommendations for future research included clarifying the differences in PI among different surgical services and using risk assessment tools that include intraoperative variables [1]
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