使用Redcap来支持对多发性硬化症患者的学习医疗保健系统的开发
M Viguera1, F Martin-Sanchez2, M E Marzo3
1U.N.E.D.
Studies in health technology and informatics
|June 30, 2023
概括
本研究开发了一个学习医疗保健系统 (LHS),通过减少诊断不确定性和提高治疗决策来改善多发性硬化症 (MS) 患者的护理. 该LHS利用患者数据提供更准确的预后,并支持临床实践.
科学领域:
- 神经科学是一个神经科学.
- 医疗信息学 医疗信息学
- 临床决策支持 临床决策支持
背景情况:
- 多发性硬化症 (MS) 呈现出多样化的表型,使治疗和预后的短期临床决策复杂化.
- 目前的多发性硬化症诊断往往是追溯的,这凸显了需要改进预测能力的需要.
- 学习医疗保健系统 (LHS) 通过数据驱动的洞察力为临床实践的持续改进提供了一个框架.
研究的目的:
- 开发一个学习型医疗保健系统 (LHS),旨在减少多发性硬化症 (MS) 管理的不确定性.
- 为了利用患者数据,在MS中提供更准确的预后和基于证据的临床决策.
- 创建一个不断改进的MS患者护理系统.
主要方法:
- 进行了文献研究,以确定MS的相关临床报告结果 (CRO) 和患者报告结果 (PRO).
- 使用ReDCAP设计了一个数据收集和管理协议,用于CRO和PRO数据.
- 300名多发性硬化患者的队列正在被跟踪18个月,并正在进行数据收集.
主要成果:
- 目前,已有93名患者被纳入该研究.
- 从患者数据中收到64个完全反应和1个部分反应.
- 收集的数据将成为开发LHS的基础.
结论:
- 开发的LHS旨在为MS患者提供准确的预后.
- 该系统旨在自动包含数据并随着时间的推移进行算法改进.
- 这种方法旨在增强临床实践,减少MS的诊断不确定性.
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