Artificial Intelligence and Machine Learning for Public Health Data Modernization: An Explainer
Public health systems across the country are undergoing data modernization, and artificial intelligence (AI) and machine learning (ML) will play a significant role in this effort.
AI refers to computer systems that can perform tasks that typically require human intelligence. ML is a subset of AI in which systems learn from data without explicit programming.
AI and ML offer transformative capabilities for public health, enhancing functions such as disease surveillance, data quality management, resource optimization, and workflow automation.
Altarum has identified five key AI/ML-enhanced use cases for advancing public health data modernization:
- Public health decision support platform
- Community health intelligence suite
- Public health workforce augmentation through AI
- AI-enhanced emergency response coordination
- Predictive population health management
These cases can best be supported by what are called “multi-agent frameworks.” In such frameworks, multiple specialized AI agents work together to solve complex problems, mirroring the ways public health teams function. This paper also includes an overview of what is required for implementation and a recommended timeline. Successful implementation of AI/ML in public health requires more than just technological implementation. It demands organizational readiness, sustained commitment from leadership, investment in workforce development, and a culture that embraces innovation while maintaining a strong ethical foundation.
Altarum is excited by the transformational opportunities of AI in public health and looks forward to supporting public health agencies with implementation.
Read Artificial Intelligence and Machine Learning for Public Health Data Modernization: An Explainer to learn more.