# Artificial Intelligence-Assisted Clinical Decision Support for Preventing Hypoglycemia in Hospitalized Patients

> **NIH NIH K23** · VANDERBILT UNIVERSITY MEDICAL CENTER · 2024 · $158,572

## Abstract

PROJECT SUMMARY
Dr. Aileen Wright, MD, MS, is an Instructor in the Department of Biomedical Informatics and Department of
Medicine at Vanderbilt University Medical Center (VUMC). Her long-term goal is to become an independent
physician scientist bridging the gap between artificial intelligence (AI) and healthcare. To this end, Dr. Wright
seeks a mentored career development award to develop an AI-assisted clinical decision support (CDS) tool for
diabetes care. Hypoglycemia is the most common complication of insulin therapy, and has been associated
with increased risk of acute coronary syndrome, stroke, falls, length of stay, and mortality. Due to the severe
consequences of hypoglycemia, insulin therapy is on the Institute for Safe Medication Practices’ list of high-risk
medications and insulin-induced hypoglycemia has been designated a “never event”. Models to predict
hypoglycemia in hospitalized patients have been developed, and could be implemented as CDS tools to
improve the safety of insulin therapy. However, published models could benefit from improvements in
accuracy. Furthermore, these models have not yet translated to tangible clinical outcomes as they have not
been integrated into the electronic health record (EHR). For AI-assisted CDS tools to be effective, they must be
developed with clinician input throughout the design process to ensure tools are utilized, fits into the clinician
workflow, and reduce clinical workload rather than increasing cognitive burden. CDS which is not accurate and
fits poorly into the clinician workflow, can contribute to ‘alert fatigue’, user dissatisfaction with the EHR, and
clinician burnout. The objective of this proposed study is to use state-of-the-art machine learning methods and
human-centered design processes to develop a high performing AI-assisted CDS tool for preventing
hypoglycemia in hospitalized, non-critically ill adults. The specific aims of this proposal are to 1) validate and
extend existing inpatient hypoglycemia models, 2) expand feature space and apply deep learning to
hypoglycemia prediction, and 3) integrate and prospectively validate prediction models for diabetes care. Dr.
Wright is a practicing general internal medicine physician who has completed a fellowship in biomedical
informatics. During the award period, Dr. Wright’s research and career objectives include broadening her
methodological foundation in machine learning to include deep learning techniques, gaining experience
integrating clinical prediction models into the EHR, and forging new collaborations in informatics and clinical
medicine. These objectives will be met through a combination of didactic coursework, mentored research, and
career development activities. This award will facilitate Dr. Wright’s transition to an independent investigator
who develops, implements, and evaluates AI-assisted CDS tools to transform healthcare, preventing harm for
patients with diabetes and lifting burdens for clinicians.

## Key facts

- **NIH application ID:** 10887161
- **Project number:** 1K23DK136974-01A1
- **Recipient organization:** VANDERBILT UNIVERSITY MEDICAL CENTER
- **Principal Investigator:** Aileen Wright
- **Activity code:** K23 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $158,572
- **Award type:** 1
- **Project period:** 2024-04-01 → 2029-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10887161

## Citation

> US National Institutes of Health, RePORTER application 10887161, Artificial Intelligence-Assisted Clinical Decision Support for Preventing Hypoglycemia in Hospitalized Patients (1K23DK136974-01A1). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10887161. Licensed CC0.

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