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

Abstract

Background

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.

Objectives

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.

Methods

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).

Results

The model area under the curve was 0.69 on the validation dataset; however, we chose not to implement the model in clinical practice.

Conclusion

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.

Keywords

electronic health records and systems - clinical decision support - inpatient care - diabetes mellitus - safety

Protection of Human and Animal Subjects

The study was performed in compliance with the World Medical Association Declaration of Helsinki on Ethical Principles for Medical Research Involving Human Subjects. The Institutional Review Board at Atrium Health Wake Forest Baptist reviewed and approved analysis and approved a full waiver of consent.

Publication History

Received: 31 December 2024

Accepted: 20 May 2025

Accepted Manuscript online:
21 May 2025

Article published online:
10 October 2025

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