STTR Phase I: An integrated platform for the analysis of patient health record data to enable predictive clinical decision support

这项小型企业技术转移（STTR）第一阶段项目的更广泛影响/商业潜力是减少可预防的患者再入院，简化分流，并早期发现多器官疾病。目前，医疗服务和护理协调的失败、过度治疗和管理的复杂性每年使美国医疗保健系统损失约3000亿美元，并且是临床环境中死亡的最大贡献者之一。最近医疗保健行业向电子健康记录的转变为通过使用临床决策支持系统来降低这些成本和死亡率提供了新的机会。然而，现有的临床决策支持系统的影响有限，部分原因是它们未能发现患者状态的趋势和忽视风险因素的相互依赖性。此外，这些系统必须定期手动更新，以应对随着时间的推移而下降的准确性。因此，迫切需要改进临床决策支持系统的基础技术。提出的技术直接解决了目前临床决策支持技术的局限性，同时没有给临床医生带来额外的负担，从而使其更容易被临床采用。此外，巨大的价值主张和挽救生命的潜力为本研究中开发的临床决策支持系统提供了广泛的商业吸引力。拟议的项目促进了对患者健康信息趋势的分析，并确定了对预测患者预后有用的生理数据之间的相关性。电子医疗记录中收集的大量患者健康信息为提高医疗保健质量提供了机会，同时也带来了与解释此类数据相关的实际挑战。这些挑战包括处理不可靠且不定期的测量结果，量化健康风险因素之间的相互依存关系，以及开发基础设施，以便将医疗记录与临床决策支持工具套件有效地连接起来。该项目需要实施复杂的数据输入程序，以修复不完美的时间序列测量，并建立趋势特征，用于疾病预测和患者转移推荐工具。趋势信息将与生命体征和实验室测试之间的相关性相结合，然后使用可靠预测患者预后的统计方案进行优化。这种分析技术与现有临床信息技术基础设施的集成将使临床医生能够更有效地利用他们可用的数据，降低与过度治疗和延长住院时间相关的成本，并改善患者的医疗保健结果。

The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase I project is to reduce preventable patient readmissions, streamline triage, and detect multi-organ diseases early. Currently, failures of care delivery and care coordination, overtreatment, and administrative complexity cost the American healthcare system an estimated 300 billion dollars per year, and are among the largest contributors to mortalities within clinical settings. The recent transition of the healthcare industry to electronic health records offers new opportunities to reduce these costs and mortalities through use of clinical decision support systems. However, existing clinical decision support systems have had limited impact, due in part to their failure to detect trends in patient status and neglect of risk factor interdependence. Further, these systems must be updated manually on a regular basis to combat declining accuracy over time. Thus, there exists a pressing need to improve the technology underlying clinical decision support systems. The proposed technology directly addresses the current limitations of clinical decision support technology while placing no additional burden on clinicians, thus easing its adoption into clinics. In addition, the large value proposition and life-saving potential lend broad commercial appeal to the clinical decision support system being developed in this study.The proposed project advances the analysis of trends in patient health information and the identification of correlations among physiological data that are useful in predicting patient outcomes. The vast amounts of patient health information that are collected in electronic medical records present opportunities for improving the quality of health care, as well as practical challenges that are associated with interpreting such data. These challenges include the processing of measurements taken unreliably and at irregular intervals, the quantification of the interdependence of health risk factors, and the development of infrastructure for effectively interfacing medical records with suites of tools for clinical decision support. This project entails the implementation of sophisticated data imputation procedures for repairing imperfect time series measurements and building trend features for use in disease prediction and patient transfer recommendation tools. Trend information will be combined with correlations between sets of vital signs and lab tests, and then optimized using a statistical scheme for reliably predicting patient outcomes. The integration of this analytic technology with existing clinical information technology infrastructure will empower clinicians to more effectively use the data available to them, reduce the costs associated with overtreatment and extended stay, and improve patient health care outcomes.