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再入院率，但美国不同且互不相连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、不同规模收集的。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，允许不同利益相关者交换健康相关数据和信息，可以大大降低成本。因此，快速医疗互操作性资源（FHIR）最近被引入作为卫生系统和信息交换的新互操作性标准，它集成了来自本地、区域和国家合作伙伴的异构数据源，以实现新一代智能健康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下创建新资源以及基于FHIR（SMART-on-FHIR）医疗保健的可替代医疗应用和可重用技术下的新系统，利用通用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。由此产生的平台具有统一的异构数据集和先​​进的数据分析功能，可以实现以下医疗保健功能：（i）在护理点决策支持方面，集成涵盖大量患者群体的异构电子病历，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) 对于需要广泛的医学知识来解释的临床决策，使用马尔可夫逻辑网络等概率方法来创建图表和规则。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； (iii) 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整合起来，以发现高流行死亡的原因，以进行公共卫生监测。因此，这个 EAGER 项目不仅集成了不同的 EMR、知识数据库和人口数据库以进行分析开发，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.