AEGIS: A robust and scalable real-time public health surveillance system

被引:30
|
作者
Reis, Ben Y.
Kirby, Chaim
Hadden, Lucy
Olson, Karen
McMurry, Andrew J.
Daniel, James B.
Mandl, Kenneth D.
机构
[1] Harvard MIT Div Hlth Sci & Technol, Childerns Hosp Informat Program, Boston, MA 02115 USA
[2] Harvard Univ, Sch Med, Dept Publ Hlth, Boston, MA 02115 USA
关键词
D O I
10.1197/jamia.M2342
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
in this report, we describe the Automated Epidemiological Geotemporal Integrated Surveillance system (AEGIS), developed for real-time population health monitoring in the state of Massachusetts. AEGIS provides public health personnel with automated near-real-time situational awareness of utilization patterns at participating healthcare institutions, supporting surveillance of bioterrorism and naturally occurring outbreaks. As real-time public health surveillance systems become integrated into regional and national surveillance initiatives, the challenges of scalability, robustness, and data security become increasingly prominent. A modular and fault tolerant design helps AEGIS achieve scalability and robustness, while a distributed storage model with local autonomy helps to minimize risk of unauthorized disclosure. The report includes a description of the evolution of the design over time in response to the challenges of a regional and national integration environment.
引用
收藏
页码:581 / 588
页数:8
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