Design and Implementation of an Automated Exposure Notification Framework: Scalable Health Monitoring for Clinical Enterprises
Year: 2025
Type: preprint
Abstract: This paper presents the design and implementation of a scalable exposure alerting and health monitoring system deployed during the COVID-19 pandemic for a large US-based healthcare provider. The system ingests structured employee health survey data, integrates it with test results and vaccination records from electronic health systems, and applies configurable business rules to identify individuals at potential exposure risk. Upon detection, the system automatically triggers follow-up workflows and sends notifications via SMS and email to affected employees and their managers. To improve scalability, the platform was later redesigned using an object-oriented approach that allowed the creation of bulk tickets, reducing the processing time from approximately one hour to 15 minutes. During its deployment, the system supported more than 13,000 employees and generated more than 15,000 workflow tickets. This work outlines the system architecture, privacy-focused design and performance impact, demonstrating how rapid engineering responses can improve workforce safety and operational readiness during public health crises.
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4411202425