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摘要: 近年来, 随着信息技术快速发展和对循证临床实践和决策的需求急剧增加, 真实世界数据在医疗健康领域的作用日益受到重视。基于真实世界数据的疾病管理研究已成为重要发展方向, 其核心是针对疾病负担、诊断、临床预防和治疗、预后管理等多个方面的疾病管理问题, 在严谨的、预先设定的设计基础上, 基于海量、多源和高质量数据开展科学的数据分析和处理, 最终将基于这些研究证据形成疾病管理的指南、路径和技术规范, 从而提高医疗质量和安全。本文系统概述了基于真实世界数据开展疾病管理研究的一些重要问题。Abstract: In recent years, with the rapid development of information technology and the increasing demand for evidence-based healthcare practice and policy decisions, the value of real-world data has received extensive attention. One of the most important applied areas of real-world data is disease management. Its core concepts lie in the application of rigorous and pre-specified designs to data of large volume, multiple sources, and high quality. By doing so, the high-quality real-world evidence for disease burden, diagnosis, prevention, treatment, and prognosis is generated to inform the development of guidelines, clinical pathways, and technical standards for disease management, which ultimately improves healthcare quality and safety. This paper systematically discussed important issues regarding the use of real-world data for research on disease management.
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Key words:
- real world data /
- disease management, research
利益冲突 无 -
表 1 不同来源真实世界数据在疾病管理研究中的优势和不足
典型数据来源 优势 不足 适宜的研究领域 备注 基于常规收集的真实世界数据 医院电子病历数据 诊疗过程数据相对最完善;医嘱信息完整,能获得院内用药数据;实验室检查数据准确性高;能获得影像学数据 无院外诊疗数据;通常无法获得患者生活行为习惯数据;无院外随访数据;数据缺失可能较多;非结构化数据需要转化和验证 疾病负担评估;防治策略评估;诊断价值评估;预后管理策略 疾病诊断准确性与医疗机构及医生诊断水平密切相关 区域性医疗数据库 能获得患者在多家医疗机构的就诊数据;能获得患者既往患病和就诊信息 医疗机构间数据质量有差异;患者链接可能不完善;药品编码可能不完善;数据缺失可能较多 疾病负担(区域内人群);病因探索;预后管理策略;部分疾病自然史 数据完整性、结构化程度与区域化数据库的成熟度密切相关 医保数据库 人群代表性好;费用数据准确性高 疾病诊断可能不准确;缺乏诊疗过程数据;无患者自报数据 疾病负担评估;部分病因探索;部分疾病自然史 往往只能获得患者主要疾病诊断,且缺乏多种混杂变量数据 主动收集的真实世界数据 患者登记 数据准确性较高;人群代表性较好;能获得患者自报数据;能够获得长期随访数据 资源需求高、研究时间长、协调管理难度相对更大 疾病自然史;疾病负担评估;病因探索;防治策略评估;预后管理策略 数据质量与研究设计、执行情况密切相关 基于实效性试验的数据 随机过程能控制多种无法观测的混杂因素 资源需求高、研究时间长、协调管理难度相对更大 防治策略评估 往往一个研究仅针对一个确切的疾病防治问题 -
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