Supplement of NIDDK R01 newer GLDs and Clinical Outcomes

项目摘要/摘要 在我们的父级奖项R01 DK133465中，我们利用了来自One佛罗里达临床研究中心的真实数据(RWD 为新的降糖药物确定临床高受益患者亚组并产生 设计政策性干预措施以改善保健质量和卫生公平的经济证据 2型糖尿病(T2D)护理。One佛罗里达州包含约2000万名患者电子病历，分布在佛罗里达州、佐治亚州和 阿拉巴马州，与包括医疗补助和医疗保险索赔在内的各种其他来源的数据相关联。我们正在制作 (1)开发识别“忠诚患者”的研究级可计算表型算法的进展， 被定义为在EHR中有充分记录的医疗接触和药物暴露的人；(2)临床识别 针对较新的GLD的高收益患者亚组；以及(3)改进我们的糖尿病微模拟模型以生成 设计政策性干预措施以改善保健质量和卫生公平的经济证据 T2D护理。在我们的续订应用中，我们的目标是构建T2D的数字孪生模型，不仅考虑到 临床特征和多方面的社会健康决定因素(SDoH)支持整合 将社会关怀转化为医疗保健服务。然而，基于AI/ML的数字双胞胎模型在计算上是 复杂且渴望数据，需要使大量真实的患者数据做好AI/ML准备。 在本行政副刊中，在目标1中，我们将制定管道和相关文件，以(A) 将RWD数据标准化为以SDoH为重点的通用数据模型，以及(B)将RWD转换为AI/ML- 现成的数据集，为开发T2D数字双胞胎模型做准备。建立在我们的T2D模拟模型上， 我们将系统地确定其他因素，重点是SDoH，这些因素将显著影响个人 护理质量和不良后果，并开发管道以从One佛罗里达州提取-转换-加载(ETL) 将电子病历数据转换为广泛采用的观察性医疗结果伙伴关系通用数据模型(OMOP 清洁发展机制)。在目标2中，我们将评估不同程度的EHR开发的AI/ML模型的潜在偏差 数据完整性。使用RWD建立AI/ML模型的一种常见做法是只选择有 更完整的数据，这可能会带来偏见。我们将对下游的AI/ML模式进行系统评估 使用算法公平度量的偏差，这对我们未来开发公平的T2D数字孪生兄弟至关重要。 该项目将使我们的NIDDK支持的项目生成的数据准备好AI/ML并直接响应 至NOT-OD-23-082，其中我们将(1)准备(用于AI/ML的SDoH信息)并采用“本体或其他 改进互操作性的标准，(2)表征可能影响对数据进行训练的AI/ML模型的偏差， 以及(3)开发“数据的文档或AI/ML再利用”。随着这一管理的成功 作为补充，我们将很好地开发T2D的数字双胞胎模型，不仅考虑到临床 支持将SDoH管理与临床护理相结合 T2D的出现，导致了美国医疗保健提供的范式转变。

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.