Cumulus:一个基于电子健康记录的联合学习系统,由快速医疗互操作资源和人工智能驱动
Andrew J McMurry1,2, Daniel I Gottlieb1,3, Timothy A Miller1,2
1Computational Health Informatics Program, Boston Children's Hospital, Boston, MA 02215, United States.
概括
Cumulus是一个开源应用程序,通过使用批量FHIR API,在医疗保健系统之间促进安全的电子健康记录 (EHR) 数据共享. 这使得可扩展的人口健康研究和人工智能驱动的洞察力,同时保持患者的隐私.
科学领域:
- 医疗信息学 医疗信息学
- 数据科学数据科学数据科学
- 公共卫生 公共卫生
背景情况:
- 大规模的电子健康记录 (EHR) 数据交换面临重大挑战.
- 跨卫生保健系统的标准化数据访问对于推进研究和公共卫生倡议至关重要.
研究的目的:
- 开发,部署和测试一个开源的,云托管的应用程序 ("听众") 访问标准化EHR数据.
- 通过使用SMART/HL7批量FHIRAPI克服大规模EHR数据交换中的障碍.
主要方法:
- 开发了Cumulus,这是一个可扩展的,用于数据共享和学习的联合模型.
- 在医疗机构的安全信封中使用集装箱化,云托管的软件.
- 集成的人工智能 (AI) 和自然语言处理 (NLP) 用于非结构化文本分析.
主要成果:
- Cumulus通过批量FHIR成功访问了EHR数据,简化了5个医疗保健系统的自动处理和共享.
- 为人口健康研究生成的综合患者计数,所有代码均可开源.
- 将数据共享限制在汇总信息的政策促进了数据共享协议.
结论:
- 通过标准的API支持,云计算和AI进步,Cumulus解决了EHR数据共享障碍.
- 该平台展示了可扩展性的潜力,以支持跨多种网络配置和用例的学习.
- 促进数据共享的提供者自主性和行政简单性.
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