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

STTR 第一阶段：用于分析患者健康记录数据以实现预测性临床决策支持的集成平台

• 批准号: 1549867
• 负责人: Ritankar Das
• 金额: $ 22.49万
• 依托单位: Dascena
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-01-01 至 2017-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1549867&HistoricalAwards=false
• 关键词: STTR; Phase; integrated; platform; analysis

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.

这个小企业技术转让（STTR）第一阶段项目的更广泛的影响/商业潜力是减少可预防的患者再入院，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失败、过度治疗和管理复杂性每年使美国医疗保健系统损失估计3000亿美元，并且是临床环境中死亡率的最大贡献者之一。医疗保健行业最近向电子健康记录的过渡提供了通过使用临床决策支持系统来降低这些成本和死亡率的新机会。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检测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，以防止随着时间的推移准确性下降。因此，迫切需要改进临床决策支持系统的基础技术。所提出的技术直接解决了当前临床决策支持技术的局限性，同时不会给临床医生带来额外的负担，从而简化了临床的采用。此外，大的价值主张和拯救生命的潜力借给广泛的商业吸引力，临床决策支持系统正在开发中的研究，拟议的项目推进分析的趋势，病人的健康信息和识别的相关性之间的生理数据，是有用的预测病人的结果。收集在电子医疗记录中的大量患者健康信息为改善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和不规则间隔的测量结果，量化健康风险因素的相互依赖性，以及开发基础设施，有效地将医疗记录与临床决策支持工具套件连接起来。该项目需要实施复杂的数据估算程序，以修复不完善的时间序列测量，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集和实验室检查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局。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，并改善患者的医疗保健结果。