Supplement of NIDDK R01 newer GLDs and Clinical Outcomes

项目概要/摘要 在我们的母公司奖项R 01 DK 133465中，我们利用来自OneFlorida+临床研究的真实世界数据（RWD） 确定新型降糖药物的临床高获益患者亚组并生成 制定政策层面干预措施以提高医疗质量和卫生公平性的经济证据 2型糖尿病（T2 D）治疗One佛罗里达+包含约2000万名患者EHR，分布在佛罗里达、格鲁吉亚和 亚拉巴马，与其他各种来源的数据，包括医疗补助和医疗保险索赔。我们正在取得 在以下方面取得的进展：（1）开发研究级可计算表型算法，用于识别“忠诚患者”， 定义为那些在EHR中有完整记录的医疗接触和药物暴露的人;（2）在临床上识别 新GLD的高获益患者亚组;（3）改进我们的糖尿病微观模拟模型， 制定政策层面干预措施以提高医疗质量和卫生公平性的经济证据 T2 D护理在我们的更新申请中，我们的目标是构建T2 D的数字孪生模型，不仅考虑 临床特征，以及健康的多方面社会决定因素（SDoH），以支持整合 将社会关怀转化为医疗服务。尽管如此，基于AI/ML的数字孪生模型在计算上 复杂且数据量大，需要将大量真实的患者数据AI/ML就绪。 在这份行政补充文件中，在目标1中，我们将制定管道和相关文件，以便： 将RWD数据标准化为公共数据模型，重点关注SDoH，以及（B）将RWD制成AI/ML- 准备好数据集，为T2 D数字孪生模型的开发做准备。基于我们的T2 D仿真模型， 我们将系统地确定其他因素，重点是SDoH，这将大大影响个人的 护理质量和不良后果，并开发从OneFlorida+提取-转换-负载（ETL）的管道 将EHR数据导入广泛采用的观察性医疗成果伙伴关系通用数据模型（OMOP CDM）。在目标2中，我们将评估使用不同程度的EHR开发的AI/ML模型的潜在偏差 数据完整性。使用RWD构建AI/ML模型的常见做法是仅选择具有以下特征的患者： 更完整的数据，这可能会引入偏见。我们将系统地评估下游AI/ML模型 使用算法公平性指标来消除偏见，这对我们未来开发公平的T2 D数字孪生模型至关重要。 该项目将使我们的NIDDK支持的项目AI/ML生成的数据准备就绪并直接响应 到NOT-OD-23-082，我们将（1）准备“用于AI/ML的SDoH信息”，并采用“本体或其他 提高互操作性的标准”，（2）表征“可能影响在数据上训练的AI/ML模型的偏见”， （3）为数据的AI/ML重用者开发文档。随着这次行政会议的成功， 作为补充，我们将有能力开发T2 D的数字双胞胎模型，不仅考虑到临床 特点，而且多方面的SDoH，以支持将SDoH管理纳入临床护理 T2 D，导致美国医疗保健服务的范式转变。

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-positioned to develop digital twin models of T2D, considering not only clinical characteristics but also multifaceted SDoH to support the integration of SDoH management into the clinical care of T2D, leading to a paradigm shift in the US health care delivery.