Data-Driven Optimisation of Microbiology Laboratory Workflows
PhD candidate: Negar Abedini
Timely and reliable diagnostic services are essential for high-quality healthcare. In microbiology laboratories, clinical samples move through a combination of automated and manual processes before final results can be reported. Because samples arrive at different times and may follow different routes, it can be challenging to manage workload, prevent delays, and use available resources efficiently.
This PhD project focuses on analysing and improving workflow dynamics in modern microbiology laboratories, in close collaboration with Labmicta. The aim is to better understand how samples move through the laboratory system and where waiting times, workload peaks, and bottlenecks occur. By using real laboratory data, the project investigates the connection between automated systems and manual work performed by laboratory technicians.
The research combines data analysis, queueing theory, simulation, and optimisation. These methods are used to model laboratory workflows, evaluate current performance, and explore possible improvements in planning and scheduling. The project pays specific attention to how different operational decisions can influence turnaround times, resource utilisation, and workload balance.
The expected outcome is a set of data-driven insights and decisionsupport methods that can help microbiology laboratories work more efficiently. In this way, the project contributes to faster diagnostic processes, better use of laboratory capacity, and improved support for healthcare professionals and patients.
