Automating infectious disease surveillance and public health reporting

Background Automated access to timely and accurate clinical information is essential for infection and sepsis management, public health surveillance, and emergency response. However, notifiable disease reporting is largely dependent on electronic laboratory data transfer and follow-up case interviews that are subject to delayed reporting and manual chart extraction that hinder timely case investigation and interfacility communication. Methods We investigated whether an artificial intelligence (AI)-based abstraction, combining interoperable electronic health record data with locally hosted large language models (LLM) in a secure cloud environment, could automatically generate high quality public health reporting forms for reportable public health diseases. Using two case studies (multidrug-resistant organism (MDRO) surveillance and dengue fever case identification and reporting), we integrated structured data and unstructured data from Fast Healthcare Interoperability Resources (FHIR) to generate automated case-report forms. Results MDRO transfer forms were reviewed by subject matter experts (board-certified infectious disease physicians) who reported no major errors in fidelity. Dengue fever case investigation forms were validated by County of San Diego Public Health Services staff confirming LLM had captured essential information with high concordance as well as additional important context that was not present in the submitted human-completed forms. Conclusions These findings demonstrate the feasibility of interoperable, AI-based workflows for case-report generation using an interoperable architecture. Further, these findings highlight opportunities to automate and scale syndromic surveillance efficiently, accelerating clinical and public health response, while supporting reliable interfacility communication.