Transforming Real-world evidence with Unstructured and Structured data to advance Tailored therapy (TRUST)

使用非结构化和结构化数据转换现实世界证据以推进定制治疗 (TRUST)

• 批准号: 10450726
• 负责人: Daniel Jay Riskin
• 金额: $ 189.54万
• 依托单位: VERANTOS, INC.
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10450726
• 关键词: Transforming; Real; world; evidence; Unstructured

Project Summary There is a national desire to implement real-world evidence (RWE) within regulatory and clinical pathways as a step toward personalized medicine, improved care, and more efficient care. This will accelerate use of routinely collected data to refine care pathways. By influencing what is approved, reimbursed, and selected for care, RWE will adjust the standard of care. But, adjusting the standard of care can have unintended and dangerous consequences. Bad data allowed into a patient’s electronic health record (EHR) has the potential to hurt one patient. Bad data allowed into regulatory or reimbursement pathways can harm a nation. RWE is often used to support trial recruitment, trial design, and marketing insight. As it is increasingly used to make clinical assertions, there is reason to believe that current approaches may benefit from greater rigor. Claims data often have accuracy below 50%. EHR problem lists often have accuracy below 60%. It is believed that low sensitivity incorporates skew since sicker patients with more touch points in the health system have more complete documentation. This program seeks to study data quality in the context of a potential drug launch. Leaders in the space intend to study data quality while testing a novel and highly rigorous approach to RWE. To achieve the goal of understanding how data quality influences RWE assertions, the proposed project includes innovations in phenotyping, gold standard, accuracy measurement, and enhanced privacy and security. This effort comes at a critical time, as regulators, payers, and providers are increasingly incorporating RWE insights into their decision-making processes. By studying data quality and demonstrating safe approaches to RWE, the country can move forward on solid footing.

项目摘要 国家希望在监管和临床领域实施真实世界证据（RWE） 路径作为迈向个性化医疗，改善护理和更有效的护理的一步。这将 加快使用定期收集的数据，以完善护理途径。通过影响批准的内容， 报销，并选择护理，RWE将调整护理标准。 但是，调整护理标准可能会产生意想不到的危险后果。不良数据 允许进入患者的电子健康记录（EHR）有可能伤害一名患者。不良数据 允许进入监管或报销途径可能会损害一个国家。 RWE通常用于支持试验招募、试验设计和营销洞察。因为人们越来越 用于作出临床断言，有理由相信，目前的方法可能受益于 更严格。索赔数据的准确率通常低于50%。EHR问题列表通常具有以下准确性 百分之六十据信，低灵敏度包含偏斜，因为病情较重的患者在患者中具有更多的触摸点。 卫生系统有更完整的文件。 该计划旨在研究潜在药物发布背景下的数据质量。太空中的领导者 打算研究数据质量，同时测试RWE的一种新颖且高度严格的方法。实现 为了理解数据质量如何影响RWE断言，建议的项目包括 在表型分析、黄金标准、精确测量以及增强隐私和安全性方面的创新。 这一努力正值关键时刻，因为监管机构，付款人和供应商越来越多地合并 RWE深入了解其决策过程。通过研究数据质量并证明安全 通过与RWE的合作，该国可以在坚实的基础上向前迈进。

项目成果

Retrospective comparison of traditional and artificial intelligence-based heart failure phenotyping in a US health system to enable real-world evidence.

• DOI: 10.1136/bmjopen-2023-073178
• 发表时间: 2023-08-09
• 期刊: BMJ OPEN
• 影响因子: 2.9
• 作者: Garan, Arthur Reshad;Monda, Keri L.;Dent-Acosta, Ricardo E.;Riskin, Daniel J.;Gluckman, Ty J.
• 通讯作者: Gluckman, Ty J.