Subgroup Analytics and Advanced Semantic Technologies to Enable Personalized Medicine

亚组分析和先进语义技术可实现个性化医疗

• 批准号: 8979535
• 负责人: Daniel Jay Riskin
• 金额: $ 22.47万
• 依托单位: VMT, INC.
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-10 至 2016-09-09
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8979535
• 关键词: Accounting; Caring; Clinical; Clinical Data; Clinical Informatics; Clinical Trials; Code; Complex; Computer Assisted; Congresses; Cost Control; Data; Data Element; Electronic Health Record; Electronics; Exclusion Criteria; Goals; Health; Health system; Healthcare; Healthcare Systems; Industry; Informatics; Intervention; Investments; Label; Maps; Measurement; Medicine; Modeling; Natural Language Processing; Outcome; Pathway interactions; Patients; Pharmacologic Substance; Phase; Process; Quality of Care; Randomized Controlled Trials; Rest; Running; Semantics; Small Business Innovation Research Grant; Statutes and Laws; Structure; Subgroup; System; Technology; Time; United States; Work; World Health; base; clinical care; clinical phenotype; cohort; commercialization; cost; data sharing; health data; improved; inclusion criteria; meetings; payment; personalized medicine; population health; public health relevance; randomized trial; standard of care; success

 DESCRIPTION (provided by applicant): United States healthcare is embroiled in a crisis of inconsistent quality and overwhelming cost. Through two administrations, the national healthcare strategy has focused on using data and technology to control costs and improve care. Congress has enacted legislation to encourage measurement of quality, sharing of data, payment based on quality of care, and transparency within the healthcare system. While these goals are bipartisan and lofty, implementation requires both hard work from health systems and robust technology. Industry has developed technologies that incrementally further the national agenda, including electronic health records, computer assisted coding, and population health analytics. Each of these supports workflow within the healthcare system and improves profit margin for healthcare organizations. But, approaches that go beyond workflow, using data to better understand clinical care, are lacking. With newly available electronic health data and a massive increase in processing power, data-driven personalized medicine is just now becoming possible. It will require advanced semantic technologies to understand clinical care strategies that have been tried in the past, but that have unknown efficacy. It will pose informatics challenges in inferring inclusion criteria, interventions, and outcomes from incomplete and poorly structured data. It will require deep clinical understanding to run real-time pragmatic clinical trials based on real world data to understand complex patients. The goal, dependent on Phase I success, is to create the first commercial system to support healthcare in running real-time pragmatic clinical trials using full clinical data. This will augment the standard of care defined by randomized controlled trials to actually tailor therapy for those complex patients that account for the majority of healthcare spends, but for whom complexity precludes tailored randomized trials.