Mandatory disease reporting by radiologists is a critical yet inefficient component of public health infrastructure. Current manual, disruptive, and unidirectional processes create a significant administrative burden for clinicians and deliver data that is often delayed and fragmented for public health agencies. This manuscript examines these workflow inefficiencies through a business process analysis, which identified key pain points including context switching, manual data entry, and a fundamental lack of systems integration. To address this, we propose a modernized framework based on the adoption of structured SNOMED CT AU coding, HL7® FHIR® standards, and API-driven interoperability. The proposed model automates reporting through event-driven triggers within radiologists’ existing systems, ensuring timely and accurate data transfer. Furthermore, it introduces a critical bi-directional feedback loop, providing clinicians with confirmation and valuable outcome data. The implementation of this integrated framework can transform mandatory reporting from a bureaucratic task into a seamless byproduct of care delivery. This promotes a collaborative partnership between clinical care and public health, ultimately enhancing the timeliness, efficiency, and overall efficacy of population health surveillance.