In modern healthcare, the value of a diagnostic innovation lies not only in its scientific merit but also in its ability to reach patients efficiently and equitably. As diagnostic technologies become increasingly sophisticated, the challenge shifts from invention to integration: how do we ensure the right tests are available, in the right place, at the right time? This is where operational research and optimisation modelling can make a real difference.
Diagnostics function within intricate networks of facilities, professionals, and decision processes. A highly accurate test can still fall short of impact if laboratories are poorly located, staff deployment is uneven, or supply chains are fragile. Operational Research offers a powerful framework for addressing these challenges. Through mathematical modelling and optimisation, we can systematically explore how to configure diagnostic services to maximise efficiency, equity, and resilience.
In my work, I develop integrated analytics approaches that combine data exploration with advanced optimisation models to guide strategic and operational planning. For instance, facility location models can help determine where to establish diagnostic hubs to serve both urban and rural populations effectively. Workforce scheduling models can balance workloads across clinicians and technicians while minimising waiting times. Resource allocation and network optimisation models can improve logistics for sample transport and the distribution of diagnostic equipment. Together, these tools allow decision-makers to test alternative scenarios and identify the most effective system designs before implementing them.
Analytics-driven planning also enables a more forward-looking understanding of diagnostic demand. By linking predictive models with optimisation algorithms, we can anticipate surges in testing needs, such as during seasonal outbreaks or public health emergencies, and plan capacity accordingly. This integration of descriptive, predictive, and prescriptive analytics provides a structured way to transform data into decisions, supporting service configurations that are not only efficient but also adaptable to change.
The potential applications of these methods extend across the diagnostic ecosystem, from local service planning to national-level preparedness. Optimisation models can support decisions about how to deploy mobile diagnostic units during emergencies, how to coordinate testing across hospitals and community settings, or how to design resilient supply chains for diagnostic equipment and consumables. By quantifying trade-offs and testing “what-if” scenarios, analytics offers decision-makers a transparent and evidence-based foundation for action.
Within the CADDA community, there is a unique opportunity to combine modelling expertise with diagnostic science to accelerate translation from the lab to the health system. Collaborative projects could, for example, explore how optimisation can support rapid deployment of diagnostics in underserved areas, or how integrated analytics can inform investment in new diagnostic technologies.
By bringing together clinicians, end users, data scientists, and modellers, CADDA provides an ideal platform to build diagnostic systems that are smart, equitable, and resilient. I look forward to connecting with colleagues interested in exploring how optimisation and analytics can help shape the future of diagnostic delivery and ensure that every innovation finds its most effective path to impact.