Old-EMR, New Tricks: Leveraging a legacy EMR for AI-based prediction of clinical deterioration for improved patient safety in a post-acute hospital

Prediction of clinical deterioration in hospital settings is essential for patient safety. While there are tools, including AI tools, for predicting deterioration in acute-care facilities, there is little developed on predicting deterioration in post-acute hospitals. These environments present challenges for use of AI predictive tools, as they typically have poor data foundations and lower digital maturity. We developed an AI model that predicts clinical deterioration in a post-acute hospital with a sensitivity of 81.5%. Analysis of the variables that most contribute to prediction of deterioration reveals a strikingly physiologic pattern, consistent with known predictors of deterioration in acute care. This both validates the clinical plausibility of our findings and also provides novel evidence that acute-care predictors of deterioration may hold more broadly in the post-acute environment. We discuss the approach to developing an AI predictive model in a digitally-immature environment, including the use of workflow analysis and a modified data analytics approach.