EAGER: Developing An Intelligent Computational-Healthcare Decision Support

尽管行政和监管强调提高医疗质量和减少医院再入院率，但美国不同的和不连接的电子健康记录系统在很大程度上仍然不兼容。此外，数据是在不同的地理位置以不同的尺度收集的。他们阻碍了美国学习型医疗体系的发展，以提高医疗质量和降低成本。据估计，通过提供允许不同利益攸关方交换健康相关数据和信息的互操作性标准，可以大大降低成本。因此，快速医疗保健互操作性资源（FHIR）最近作为卫生系统和信息交换的新互操作性标准被引入，它集成了来自地方、区域和国家合作伙伴的异构数据源，以支持新一代智能卫生决策支持系统的开发。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 （SMART-on-FHIR）的可替代医疗应用和可重用技术下为医疗保健创建新系统，利用公共数据模型将不同的健康数据源协调到支持FHIR的数据库中，并与基线电子数据捕获数据库进行比较。由此产生的具有统一异构数据集和高级数据分析的平台可以为医疗保健提供以下功能：(i)在护理点的决策支持方面，集成了涵盖大量患者的异构电子病历，以识别时间序列数据和离散临床特征，以预测未来患者的急性医疗事件；（ii）对于需要广泛医学知识进行解释的临床决策，使用概率方法（如马尔可夫逻辑网络）来创建图和规则。然后将这些规则与EMR数据挖掘相结合，以提高最终决策的临床相关性；（三）在公共卫生政策决策支持方面，将美国不同州不兼容的电子死亡记录与电子病历相结合，以发现高流行死亡率的原因，以便进行公共卫生监测。因此，该EAGER项目不仅集成了用于分析开发的不同电子病历、知识库和人口数据库，而且还评估了FHIR标准的功能和限制。

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.