An AI-Based Public Health Data Monitoring System
This work addresses scalable data monitoring for public health experts, representing a domain-specific improvement.
The paper tackled the problem of monitoring large volumes of public health data for outbreaks or quality issues by proposing a ranking-based monitoring paradigm using AI anomaly detection, resulting in a 54x increase in reviewer speed efficiency compared to traditional methods.
Public health experts need scalable approaches to monitor large volumes of health data (e.g., cases, hospitalizations, deaths) for outbreaks or data quality issues. Traditional alert-based monitoring systems struggle with modern public health data monitoring systems for several reasons, including that alerting thresholds need to be constantly reset and the data volumes may cause application lag. Instead, we propose a ranking-based monitoring paradigm that leverages new AI anomaly detection methods. Through a multi-year interdisciplinary collaboration, the resulting system has been deployed at a national organization to monitor up to 5,000,000 data points daily. A three-month longitudinal deployed evaluation revealed a significant improvement in monitoring objectives, with a 54x increase in reviewer speed efficiency compared to traditional alert-based methods. This work highlights the potential of human-centered AI to transform public health decision-making.