In the Northern Territory of Australia, hearing health services operate within a uniquely complex environment defined by vast geography, diverse community needs, and the logistical challenges of delivering specialist care to remote regions. Existing outreach models, strong local knowledge, and established prioritisation processes provide a solid foundation for service delivery. As service demand evolves and telehealth capacity expands, there is an opportunity to enhance current planning systems through advanced analytical tools that support long‑term operational, financial, and environmental sustainability.
This use case proposes a Service-System Digital Twin—an AI-enabled simulation model that enhances existing planning by modelling client flow across audiology, teleotology, ENT, and preventive care pathways. Using discrete event simulation (DES), the digital twin integrates community demographics, referral patterns, workforce capacity, travel and seasonal access data, and preventive activity to create a dynamic environment for “what-if” scenario analysis. Planners can explore how outreach schedules, workforce configurations, telehealth expansion, and travel routes influence service timing, resource use, costs, fuel consumption, and vehicle travel before changes are implemented in practice.
A key innovation is the integration of Indigenous governance, with the Aboriginal Community Controlled sector providing input on model scope, cultural safety principles, and interpretation of outputs. The digital twin functions as a transparent decision‑support tool, generating interpretable metrics such as service coverage, waiting times by priority category, workforce utilisation, projected demand, and travel‑related efficiency. By embedding financial and sustainability considerations directly into service modelling, the approach supports responsible resource management while strengthening future service planning.