Artificial Intelligence for Cluster Detection and Targeted Intervention in Healthcare: An Interdisciplinary System Approach
Authors: Seitzinger Patrick , Rafid-Hamed Zoher , Kalra Jay 
Published: June 2024

Early detection of clusters of health conditions is essential to proactive clinical and public health interventions. Effective intervention strategies require real-time insights into the health needs of the communities. Artificial Intelligence (AI) systems have emerged as a promising avenue to detect patterns in health indicators at an individual and population level. The purpose of this paper is to describe the novel expanded application of AI to detect clusters in health conditions and community health needs to facilitate real-time intervention and prevention strategies. Case-use examples demonstrate the capabilities of AI to harness a variety of data to improve health outcomes in conditions ranging from infectious diseases, non-communicable diseases, and mental health disorders. AI systems have been utilized in syndromic surveillance to detect cases of infectious diseases prior to laboratory-confirmed diagnosis. These AI systems can analyze data from healthcare facilities, laboratories, and online self-reported symptoms to detect potential outbreaks and facilitate timely vaccination, resource allocation and public health messaging to mitigate the spread of disease. Similarly, the spread of vector-borne diseases can be anticipated through the analysis of historical data, weather reports and incidence of disease to identify areas to deploy vector control measures. In the area of mental health, AI algorithms can analyze diverse data sources such as social media posts, emergency hotline calls, emergency department visits, and hospital admissions to identify clusters related to mental health issues including overdoses, suicides, and burnout. The timely detection of such clusters enables prompt intervention, facilitating deployment of targeted mental health support services and community outreach programs to address these issues in a targeted and proactive manner. Identifying trends and characteristics in chronic disease data can guide screening and intervention strategies in real time. Similarly, AI can enhance pharmacovigilance by identifying previously unknown patterns in adverse drug reactions to inform regulatory bodies, healthcare providers and researchers in efforts to provide data-driven, real-time patient safeguards. By harnessing data from air-quality monitors, health records, and meteorology reports, AI systems identify correlations between environmental factors and health issues to empower efforts to address specific environmental health risks. These case-use examples illustrate the potential for AI to serve as a valuable tool to facilitate real-time, data-driven insights to inform proactive clinical and public health intervention strategies. Ongoing challenges in harnessing AI technology for public health surveillance include data privacy, accessing quality data from diverse data sets, and establishing effective communication channels between AI systems and public health authorities. The use of anonymized data to detect clusters and identify the health needs of health regions is a potential strategy to mitigate these challenges. Available resources are limited and must be deployed in a targeted, informed, and timely manner to be most effective. The integration of AI into an expanded all-risks approach to syndromic surveillance represents the next step in identifying and responding to clusters of health-related events in a proactive manner that aligns with community needs while upholding ethical standards and privacy considerations.