Multiple Chronic COnditions: MultiPle dAta SouRcEs (MC COMPARE)

多种慢性病：多种数据源（MC COMPARE）

• 批准号: 10726452
• 负责人: DAVID A. DORR
• 金额: $ 124.25万
• 依托单位: OREGON HEALTH & SCIENCE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10726452
• 关键词: Acute; Acute Renal Failure with Renal Papillary Necrosis; Address; Adverse event; American; Award; Benefits and Risks; Blood Pressure; Caring; Chronic; Chronic Kidney Failure; Clinical; Clinical Data; Clinical Research; Clinical Trials; Collaborations; Data; Data Set; Data Sources; Decision Making; Effectiveness of Interventions; Elderly; Electronic Health Record; Electronics; Equilibrium; Evaluation; FAIR principles; Fast Healthcare Interoperability Resources; Goals; Guidelines; Health behavior and outcomes; Hypertension; Hypotension; Infrastructure; Institution; Journals; Kidney Failure; Measurement; Measures; Medical Informatics; Methods; Modeling; Myocardial Infarction; Natural Language Processing; New York; Outcome; Outcome Measure; Outcome Study; Paper; Participant; Patient-Centered Care; Patient-Focused Outcomes; Patients; Persons; Population; Primary Care; Process; Provider; Readiness; Research; Research Personnel; Resources; Scanning; Site; Stroke; Techniques; Text; Work; adjudication; adverse event risk; adverse outcome; blood pressure control; cognitive function; collaborative approach; data harmonization; data integration; electronic data; falls; health record; hypertension control; improved; interoperability; multiple chronic conditions; multiple data sources; optical character recognition; participant enrollment; patient engagement; patient prognosis; personalized decision; preference; social health determinants; symposium; tool; usability

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.

MC COMPARE 多种慢性病：多种数据源项目摘要 指导患有多种慢性病 (MCC) 的患者进行风险收益决策的证据是 例如，慢性肾病患者可以从严格的血压控制中受益，但效果有限。 还存在不良事件的风险——低血压、跌倒、急性肾损伤（AKI）——使治疗变得困难。 正在探索如何最好地治疗包括高血压在内的 MCC 患者：A 管理高血压的协作方法 (HTN)：COACH 面向协作的方法 控制高血压（Dorr 和 Koopman）和电子工具以提高识别度和 改善慢性肾病（CKD；Samal 和 Dykes）高血压的初级保健管理。 在此提案中，我们将把来自多个机构的 EHR 数据汇总到 eCarePlan（一个应用程序）中 旨在使用 Fast Healthcare Interoperable 将多个数据源整合在一起以进行护理计划 资源（FHIR）。这些增强的数据集将帮助我们量化聚合碎片的方式。 数据可以改善老年人的临床试验结果和个性化决策。 数据集，以更好地了解这些研究中发生不良事件的风险，例如跌倒和 AKI，以及不良事件 临床结果，如中风和认知功能恶化，我们将按照以下进行。 首先，我们将自动调整和实施 eCarePlan 工具。 提取关于 HTN 和 CKD 的两项合作研究所需的数据，我们将重点关注未被识别的慢性病。 条件、合作伙伴研究变量、合作伙伴研究结果、健康行为、健康的社会决定因素、 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和扫描中提取和协调数据 其次，我们将使用以下方法来协调数据并完善集成流程 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 和提供者了解如何将这些综合研究与患者的预后和需求相匹配。 第三，我们将重复测量这两项研究的研究标准和结果。 由此产生的可互操作的基础设施对所有人开放。