Artificial Intelligence (AI) and machine learning (ML) in risk prediction of hospital acquired pressure injuries (HAPIs) among oncology inpatients.

Abstract

309 Background: Utilizing AI and ML is an emerging method to improve risk identification, characterization and stratification for clinical outcomes such as HAPIs. The Jvion Cognitive Clinical Success Machine (CCSM) utilizes the Eigen Sphere technique to factor in clinical, socioeconomic, and behavioral covariates at the individual patient level to maximize accuracy of risk prediction and provide insights on prevention of HAPIs. Methods: A retrospective analysis was performed utilizing claims and EHR data on 63,476 inpatient admissions between June 2016 and June 2018 at M D Anderson Cancer Center, a 660-bed oncology facility (Table 1) . All risk assessment indicators in the data were removed prior to analysis by the CCSM to compute unbiased risk probabilities for HAPIs. The performance of the CCSM risk prediction for all new stage 2 and above HAPIs (>=Stage 2 HAPIs) was compared with the Braden scale using AUC. Results: The Jvion CCSM had an AUC of 0.84 compared to the AUC of 0.72 for the Braden scale in the prediction of >=Stage 2 HAPIs. Conclusions: The AUC indicates that the Jvion CCSM has better predictive accuracy than the Braden scale. It also has better discrimination in risk identification. Thus, Jvion CCSM can be a valuable tool in risk screening for HAPIs which can lead to early preventative interventions. Characteristics of admission records used to compare accuracy of Jvion CCSM and Braden scale in predicting new >=Stage 2 HAPIs (N=58,228). [Table: see text]