Healthcare support services are under increasing pressure to improve efficiency, workforce safety, sustainability, and service quality while operating within constrained budgets. Traditional waste and linen collection models are typically based on scheduled rounds, resulting in unnecessary servicing, increased manual handling activities, inefficient labour deployment, and limited visibility of operational demand.
The objective of this project was to evaluate whether Internet of Things (IoT) waste and linen sensors could enable a transition from a time-based servicing model to a demand-led approach, improving operational efficiency while maintaining service standards and reducing costs.
ISS, in partnership with Eastern Health, implemented a six-month pilot program across four wards and two hospital floors using eight Internet of Things sensors connected to a live monitoring dashboard. Sensors were installed on waste bins and soiled linen receptacles located furthest from operational loading zones to assess travel time, servicing frequency, and workload distribution. The sensors provided continuous real-time fill-level monitoring and generated automated alerts when receptacles reached predefined capacity thresholds, enabling responsive and data-driven service scheduling.
The pilot identified clear overservicing and measurable inefficiencies, with some areas receiving up to six visits per day despite consistently low fill levels. Following validation, the solution was expanded across hospital buildings, with a total deployment of 70 Internet of Things sensors, transitioning operations to a demand-driven service model where tasks are generated only when receptacles reach 75% capacity.
To improve operational visibility and decision-making, the dashboard was enhanced with building and level drill-down functionality, trend analysis, heat mapping, capacity monitoring, date filtering, and exportable reporting. These innovations enabled real-time visibility of service demand, improved identification of peak workload periods, and supported workforce planning, rostering optimisation, and performance management across multiple facilities.
A key innovation of the project was the integration of clinical waste governance controls. Analysis identified opportunities to improve compliance with contractual minimum weight requirements for clinical waste bins. Barcode scanning and loading dock weighing scales were introduced to verify bin weights prior to disposal, improving traceability, audit compliance, and contract governance, while reducing avoidable disposal costs.
The implementation delivered measurable operational improvements, including labour efficiencies equivalent to a reduction in full-time equivalent (FTE) positions while maintaining service standards. Financial modelling identified cumulative net savings of approximately $612,000 over five years and $1.38 million over ten years after implementation and operating costs. Additional measurable outcomes included reduced manual handling exposure, improved workforce safety, enhanced transparency of service demand, and strengthened data-driven decision-making.
This project demonstrates how Internet of Things-enabled monitoring delivers practical operational transformation through real-time data, predictive servicing, and evidence-based resource allocation. The approach provides a scalable and replicable model for smart hospital operations, delivering sustained improvements in efficiency, safety, compliance, and financial performance.