Multiple Chronic COnditions: MultiPle dAta SouRcEs (MC COMPARE)

MC COMPARE多重慢性疾病：多重数据源项目总结 指导患有多种慢性疾病（MCC）的患者做出风险-获益决策的证据如下： 有限公司例如，患有慢性肾病的患者可以从严格的血压控制中受益，但 也存在不良事件的风险-低血压、福尔斯、急性肾损伤（阿基）-使得治疗困难。试验 正在探索如何最好地治疗MCC患者，包括高血压： 高血压管理的协作方法（HTN）： 控制高血压（Dorr和Koopman）和电子工具，以提高认识和 改善慢性肾脏病（CKD; Samal和Dykes）高血压的初级保健管理。 在本提案中，我们将把来自多个机构的电子健康记录数据汇总到eCarePlan中， 旨在使用Fast Healthcare Interoperable将多个数据源整合到一起，以进行护理规划 资源（FHIR）。这些增强的数据集将帮助我们量化聚合碎片化的方式， 数据可以改善老年人的临床试验结果和个性化决策。然后我们会用这个 数据集，以更好地了解这些研究中不良事件的风险，如福尔斯和阿基，以及不良事件 临床结果，如中风和认知功能恶化。我们将按照 数据统一的既定步骤。首先，我们将调整和实施eCarePlan工具， 提取HTN和CKD两项合作研究所需的数据。我们将重点关注未识别的慢性 条件，伙伴研究变量，伙伴研究结果，健康行为，健康的社会决定因素， 和不良事件结果。我们将使用光学字符识别（OCR）和自然语言 处理（NLP）从电子自由文本笔记中提取和协调数据， 第二，我们将协调数据，并使用 可查找性/可访问性/互操作性/可重用性（FAIR）和数据准备框架，并吸引患者参与 和提供者了解这些综合研究如何与患者预后和需求相匹配。 第三，我们将重复测量两项研究的研究标准和结果。我们将提供 从而形成面向所有人的可互操作基础设施。

MC COMPARE Multiple Chronic COnditions: MultiPle dAta SouRcEs Project Summary The evidence to guide risk-benefit decisions for patients living with multiple chronic conditions (MCCs) is limited. For example, patients with chronic kidney disease can benefit from tight blood pressure control but are also at risk of adverse events – hypotension, falls, acute kidney injury (AKI) – making treatment difficult. Trials exploring how best to treat patients with MCC including high blood pressure are being performed: A collaborative approach to managing hypertension (HTN): COACH Collaboration-Oriented Approach to Controlling High blood pressure (Dorr and Koopman) and Electronic Tools to Increase Recognition and Improve Primary Care Management for Hypertension in Chronic Kidney Disease (CKD; Samal and Dykes). In this proposal, we will aggregate EHR data from multiple institutions into the eCarePlan, an application intended to bring multiple data sources together for care planning using Fast Healthcare Interoperable Resources (FHIR). These enhanced data sets will help us to quantify the ways that aggregating fragmented data can improve clinical trial results and personalized decision making for older adults. We will then use this dataset to better understand the risk of adverse events in these studies, like falls and AKI, as well as poor clinical outcomes, like strokes and worsening cognitive function. We will proceed in accordance with established steps of data harmonization. First, we will adapt and implement the eCarePlan tool to automatically extract data needed for the two partner studies on HTN and CKD. We will focus on unrecognized chronic conditions, partner study variables, partner study outcomes, health behaviors, social determinants of health, and adverse event outcomes. We will use optical character recognition (OCR) and natural language processing (NLP) to extract and harmonize data from electronic free text notes and scanned documents.Second, we will harmonize data and refine the integration process using Findability/Accessibility/Interoperability/Reusability (FAIR) and data readiness frameworks and engage patients and providers to understand how these integrated studies can be matched to patient prognosis and needs. Third, we will replicate measurement of study criteria and outcomes for both studies. We will provide the resulting interoperable infrastructure openly for all.