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

这一小型企业技术转让(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重新入院，简化分诊，并及早发现多器官疾病。目前，护理提供和护理协调的失败、过度治疗和管理复杂性每年给美国医疗系统造成约3000亿美元的损失，是临床环境中死亡的最大贡献者之一。医疗保健行业最近向电子健康记录的过渡提供了新的机会，通过使用临床决策支持系统来降低这些成本和死亡率。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检测到患者状态的趋势，并忽视了风险因素的相互依赖。此外，这些系统必须定期手动更新，以应对精度随着时间的推移而下降的问题。因此，迫切需要改进支撑临床决策支持系统的技术。建议的技术直接解决了目前临床决策支持技术的局限性，而不会给临床医生带来额外的负担，从而使其更容易进入临床。此外，巨大的价值主张和救生潜力为本研究开发的临床决策支持系统提供了广泛的商业吸引力，该项目促进了对患者健康信息趋势的分析和对预测患者预后有用的生理数据之间的相关性的识别。电子病历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，也带来了与解释这些数据相关的实际挑战。这些挑战包括处理不可靠和不定期进行的测量，量化健康风险因素的相互依存关系，以及开发基础设施以有效地将医疗记录与临床决策支持工具套件对接。该项目需要实施复杂的数据补偿程序，以修复不完美的时间序列测量，并建立用于疾病预测和患者转移建议工具的趋势特征。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，然后使用统计方案进行优化，以可靠地预测患者结果。这项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使用他们可用的数据，降低与过度治疗和延长住院时间相关的成本，并改善患者的健康护理结果。

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.