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.

 描述（由适用提供）：美国医疗保健陷入了质量不一致和成本不一致的危机。通过两个政府，《国家医疗保健战略》致力于使用数据和技术来控制成本和改善护理。国会颁布了立法，以鼓励衡量质量，数据共享，基于护理质量的付款以及医疗保健系统内的透明度。尽管这些目标是两党和崇高的，但实施既需要卫生系统和强大的技术努力工作。行业已经开发了逐步进一步进一步促进国家Agerda的技术，包括电子健康记录，计算机辅助编码和人口健康分析。这些都支持医疗保健系统中的工作流程，并提高医疗保健组织的利润率。但是，缺乏超越工作流程，使用数据更好地了解临床护理的方法。随着新可用的电子健康数据和处理能力的大量增加，数据驱动的个性化药物现在正变得可能。它将需要先进的语义技术才能了解过去尝试过的临床护理策略，但效率未知。它将在推断不完整和结构不足数据的纳入标准，干预措施和结果方面提出信息挑战。这将需要深入的临床理解，以根据现实世界数据进行实时实用临床试验，以了解复杂的患者。取决于第一阶段成功的目标是创建第一个使用完整的临床数据运行实时实用临床试验的医疗保健的商业系统。这将增强由随机对照试验定义的护理标准，以实际量身定制那些占医疗保健支出的复杂患者的疗法，但对于他们来说，复杂性排除了量身定制的随机试验。