EAGER: Developing An Intelligent Computational-Healthcare Decision Support

尽管行政和监管部门强调提高医疗质量和减少再入院，但美国不同且不连贯的电子健康记录系统在很大程度上仍然不兼容。此外，数据是在不同地理位置以不同比例收集的。它们阻碍了美国学习型医疗保健系统的发展，以提高医疗保健质量和降低成本。据估计，通过提供互操作性标准，使不同的利益攸关方能够交换与健康有关的数据和信息，可以大大降低成本。因此，快速医疗保健互操作性资源（FHIR），它集成了来自地方，区域和国家合作伙伴的异构数据源，使新一代智能健康决策支持系统的发展，最近已被引入作为一个新的互操作性标准的卫生系统和信息交换。该项目旨在开发一个智能计算健康平台，协调各种电子病历（EMR），本体和人口健康数据库等异构数据源，并部署快速数据分析模型，以改善临床护理点和全球公共卫生政策决策支持。具体来说，这个智能计算健康平台的开发包括在FHIR下创建新资源，在基于FHIR的替代医疗应用和可重用技术（SMART-on-FHIR）下创建新系统，利用通用数据模型将不同的健康数据源协调到FHIR支持的数据库中，并与基线电子数据采集数据库进行比较。所产生的具有协调的异构数据集和高级数据分析的平台可以实现以下医疗保健功能：（i）在护理点的决策支持方面，整合覆盖大量患者人群的异构EMR以识别时间序列数据和离散临床特征，从而预测未来患者的急性医疗事件;（ii）对于需要广泛医学知识才能解释的临床决策，我们采用概率方法（例如马尔可夫逻辑网络）来制作图表和规则。这些规则，然后结合电子病历数据挖掘，以提高最终决策的临床相关性;和（iii）在公共卫生政策的决策支持，不兼容的电子死亡记录在美国不同的州与电子病历整合，以发现高流行率的死亡原因，公共卫生监测。因此，EAGER项目不仅整合了不同的EMR、知识数据库和人口数据库，用于分析开发，还评估了FHIR标准的能力和限制。

Despite the administrative and regulatory emphasis on increasing healthcare quality and reducing hospital readmissions, the disparate and disconnected electronic health record systems in US remain largely incompatible. In addition, data is collected at varying geographic locations with different scales. They hinder the development of learning healthcare systems in US for healthcare quality improvement and cost reduction. It was estimated that by providing interoperability standard that allows different stakeholders to exchange health related data and information, the cost can be tremendously reduced. Thus, Fast Healthcare Interoperability Resources (FHIR), which integrates heterogeneous data sources from local, regional, and national partners to enable the development of a new generation of intelligent health decision support systems, has recently been introduced as a new interoperability standard for health systems and information exchange. This project aims to develop an intelligent computational-health platform that harmonizes heterogeneous data sources such as various electronic medical records (EMRs), ontologies, and population-health databases, and deploys fast data analytics models to improve clinical point-of-care and global public health policy decision support. Specifically, the development of this intelligent computational-health platform includes creating new resources under FHIR and new systems under Substitutable Medical Applications and Reusable Technologies based on FHIR (SMART-on-FHIR) for healthcare, leveraging common data models to harmonize different health data sources into FHIR-enabled databases, and comparing with baseline electronic data capture databases. The resulting platform with harmonized heterogeneous datasets and advanced data analytics can enable the following functions for healthcare: (i) on decision support at the point-of-care, heterogeneous EMRs covering large patient population are integrated to identify both time series data and discrete clinical features to predict acute medical event for future patients; (ii) on clinical decision making that requires extensive medical knowledge for interpretation, probabilistic methods such as Markov Logic Network are used to create graphs and rules. Such rules are then combined with EMR data mining to improve clinical relevance of final decisions; and (iii) on public health policy decision support, incompatible electronic death records in different States in US are integrated with EMRs to discover the causes of high prevalent deaths for public health surveillance. Thus, this EAGER project not only integrates different EMRs, knowledge database, and population databases for analytics development, but also assesses the capabilities and constraints of the FHIR standard.