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Published: July 1, 2025
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In Healthbeat, Dr. Jay Varma explains how artificial intelligence can strengthen infectious disease surveillance—automating lab report processing, cleaning and connecting fragmented data, and producing real-time forecasts—while emphasizing the importance of privacy, legal safeguards, and secure design.
The Challenge of Disease Surveillance
Surveillance is the foundation of public health. It tracks outbreaks, informs policy, and evaluates whether prevention measures are working. But as Dr. Varma notes, most systems are outdated and inefficient, relying on faxes, phone calls, and manual data entry. Even well-funded health departments spend enormous time cleaning, consolidating, and analyzing data.
AI offers a chance to modernize this process—allowing public health to act faster, more accurately, and with fewer staff.
Smarter Data Collection and Reporting
Laboratories generate enormous volumes of diagnostic data, yet much of it is inconsistently formatted or manually transcribed.
- Natural language processing (NLP): AI can scan free-text lab reports, extract key details (pathogen, test, result, demographics), and automatically format them to meet reporting standards.
- Automated detection: AI can monitor lab output and instantly flag reportable cases, reducing delays from manual review.
- Compliance monitoring: AI “agents” can track whether labs are reporting as required, flagging unusual gaps and even issuing reminders under human supervision.
Cleaning and Connecting Data Sources
Surveillance data often arrive incomplete or duplicated. AI can:
- Merge duplicate reports even when names, dates, or identifiers don’t match perfectly.
- Consolidate multiple test results for the same patient.
- Fill gaps by connecting to immunization registries, hospital records, or death certificates where permitted by law.
- Conduct automated outreach: surveys or chatbot interviews sent to patients to gather missing details about exposures, travel, or symptoms.
This helps epidemiologists spend less time fixing data and more time interpreting it.
“AI won’t replace epidemiologists—but it can help them act faster, with greater accuracy, when lives are on the line.” —Dr. Jay Varma
Faster Analysis and Communication
Beyond data management, AI can help agencies actually use the information:
- Forecasting: AI can merge surveillance data with emergency room visits, 911 calls, or pharmacy sales to “nowcast” and “forecast” disease trends.
- Tailored reporting: Generative AI can create customized summaries for different audiences—technical briefs for policymakers, plain-language updates for the public, or targeted alerts for clinicians.
This allows for more transparent, usable, and timely communication of public health threats.
Privacy and Security
Surveillance data is generally exempt from HIPAA, but is still governed by strict state and local rules. Dr. Varma stresses that:
- AI systems must be run in secure, closed environments.
- Identifiable information must not be used for training or stored outside legal frameworks.
- AI can even help with safe transparency—automatically de-identifying data and testing for re-identification risks before public release.
What Comes Next
For AI to make a real impact, agencies need validated, adaptable tools designed with input from epidemiologists. Partnerships with AI developers and policymakers are essential to build systems that improve speed, quality, and usability—while maintaining security and trust.
AI can’t replace epidemiologists, but it can give them the capacity to act faster and more effectively when lives are at stake.
📅 Publication Date & Outlet
July 1, 2025 | Healthbeat (Guest Essay by Dr. Jay K. Varma)
