Subgroup Analytics and Advanced Semantic Technologies to Enable Personalized Medicine

 描述(申请人提供)：美国医疗保健陷入了质量不一致和成本过高的危机。通过两届政府，国家医疗保健战略的重点是使用数据和技术来控制成本和改善医疗保健。国会已颁布立法，鼓励医疗保健系统内的质量衡量、数据共享、基于护理质量的支付和透明度。虽然这些目标是两党合作和崇高的，但实施既需要卫生系统的艰苦工作，也需要强大的技术。工业界开发了逐步推进国家议程的技术，包括电子健康记录、计算机辅助编码和人口健康分析。其中每一项都支持医疗保健系统内的工作流程，并提高医疗保健组织的利润率。但是，缺乏超越工作流的方法，使用数据来更好地理解临床护理。随着新的电子健康数据的出现和处理能力的大幅提高，数据驱动的个性化医疗刚刚成为可能。它将需要先进的语义技术来理解临床护理策略，这些策略在过去已经尝试过，但疗效未知。这将对从不完整和结构不良的数据中推断纳入标准、干预措施和结果提出信息学挑战。这将需要深入的临床理解，以基于真实世界的数据进行实时、务实的临床试验，以了解复杂的患者。取决于第一阶段的成功，目标是创建第一个商业系统，以支持医疗保健使用完整的临床数据进行实时实用的临床试验。这将增加随机对照试验定义的护理标准，以实际为那些复杂的患者量身定做治疗，这些患者占医疗保健支出的大部分，但其复杂性排除了定制随机试验的可能性。

 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.