Challenges with Implementing Predictive Models for Inpatient Hypoglycemic Events in Clinical Decision Support

Inpatient hypoglycemia is associated with increased length of stay and mortality. There have been several models developed to predict a patient's risk of inpatient hypoglycemia.This study aimed to describe the barriers to implementing a model that we developed to predict inpatient hypoglycemic events informing a clinical decision support tool.A logistic regression model was trained on inpatient hospitalizations of diabetic patients receiving insulin at Atrium Health Wake Forest Baptist Medical Center, an academic medical center in the Southeastern United States, from January 2020 to December 2021. The model was developed to predict a hypoglycemic event (glucose < 70 mg/dL) within 24 hours of a patient's first borderline-low glucose measurement (70-90 mg/dL).The model area under the curve was 0.69 on the validation dataset; however, we chose not to implement the model in clinical practice.We decided not to implement our predictive model into clinical decision support due to a variety of factors including limitations in the predictiveness of the model and several contextual factors. Through this work we learned that it is not always feasible to use predictive analytics in clinical decision support, especially when attempting to predict low incidence events for which some important predictors are not documented in the electronic health record in a structured way.