# Supplement of NIDDK R01 newer GLDs and Clinical Outcomes

> **NIH NIH R01** · UNIVERSITY OF FLORIDA · 2023 · $290,798

## Abstract

PROJECT SUMMARY/ABSTRACT
In our parent award R01 DK133465, we leverage real-world data (RWD) from the OneFlorida+ Clinical Research
Consortium to identify clinically high-benefit patient subgroups for newer glucose-lowering drugs and generate
economic evidence for designing policy-level interventions to improve the quality of care and health equity in
type 2 diabetes (T2D) care. OneFlorida+ contains ~20 million patient EHRs across Florida, Georgia, and
Alabama, linked with data from various other sources, including Medicaid and Medicare claims. We are making
progress on (1) developing research-grade computable phenotype algorithms for identifying “loyal patients,”
defined as those with medical encounters and drug exposure fully documented in EHRs; (2) identifying clinically
high-benefit patient subgroups for newer GLDs; and (3) refining our diabetes microsimulation model to generate
economic evidence for designing policy-level interventions to improve the quality of care and health equity in
T2D care. In our renewal application, we aim to construct digital twin models of T2D that consider not only
clinical characteristics but also the multifaceted social determinants of health (SDoH) to support the integration
of social care into health care delivery. Nevertheless, AI/ML-based digital twin models are computationally
complex and data-hungry, requiring to make large amounts of real-world patient data AI/ML-ready.
In this administrative supplement, in Aim 1, we will develop pipelines and associated documentation to (a)
standardize RWD data into a common data model with a focus on the SDoH, and (b) make the RWD into AI/ML-
ready datasets, in preparation for the development of T2D digital twin models. Built on our T2D simulation model,
we will systematically identify additional factors, with a focus on SDoH, that would significantly affect individuals’
quality of care and adverse outcome, and develop pipelines to extract-transform-load (ETL) from the OneFlorida+
EHR data into the widely adopted Observational Medical Outcomes Partnership Common Data Model (OMOP
CDM). In Aim 2, we will evaluate the potential bias of AI/ML models developed with different degrees of EHR
data completeness. A common practice in building AI/ML models using RWD is to select only patients that have
more complete data, which may introduce bias. We will systematically assess the downstream AI/ML model
bias using algorithmic fairness metrics, which is critical for our future development of a fair T2D digital twin.
This project will make the data generated from our NIDDK-supported project AI/ML-ready and respond directly
to NOT-OD-23-082, where we will (1) prepare “SDoH information for use in AI/ML” and adopt “ontologies or other
standards to improve interoperability,” (2) characterize “biases that may affect AI/ML model trained on the data,”
and (3) develop “documentation for or AI/ML re-users of the data.” With the success of this administrative
supplement, we will be well-posit...

## Key facts

- **NIH application ID:** 10842681
- **Project number:** 3R01DK133465-02S1
- **Recipient organization:** UNIVERSITY OF FLORIDA
- **Principal Investigator:** Jingchuan Guo
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $290,798
- **Award type:** 3
- **Project period:** 2022-07-20 → 2026-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10842681

## Citation

> US National Institutes of Health, RePORTER application 10842681, Supplement of NIDDK R01 newer GLDs and Clinical Outcomes (3R01DK133465-02S1). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10842681. Licensed CC0.

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