Improving clinical chemistry laboratory logistics, Symposium and PhD thesis defence by Eline Tsai

Prior to the PhD defence of Eline Tsai on the 22^nd of May, 2026 of the thesis “Improving clinical chemistry laboratory logistics” there will be a short symposium. Talks will be given by dr. Gréanne Leeftink (CHOIR & UMC Utrecht), dr. Andrei N. Tintu (Erasmus MC), prof. dr. Robert de Jonge (Amsterdam UMC), dr. Nashwan Al Naiemi (Labmicta) and Negar Abedini (CHOIR). 

The symposium will include a lunch break after the talks, where lunch will be provided for registered attendees.

Program:

Abstracts

Optimizing histopathology processes - dr. Gréanne Leeftink

Histopathology laboratories strive for minimal diagnostic turnaround times within a workflow that relies on both automated batch processes and manual operations. We developed an ILP model to optimize the pathology workflow at UMC Utrecht, aimed at simultaneously reducing turnaround times and leveling the workload. The model optimizes the timing of activities, including the start times of batch machines, taking into account staff capacity and process dependencies. The results show that a significant reduction in morning peak load and a more balanced distribution of work throughout the day can be achieved, thereby freeing up capacity for a future increase in requests. Additionally, a reduction in total throughput time of 2–8 hours per request is realized.

Lab_2_Data_2_Lab: The Strategic Evolution of Laboratory Medicine - dr. Andrei N. Tintu 

Diagnostic laboratories are undergoing a fundamental paradigm shift, evolving from a physical sample-processing facility into a high-throughput data ecosystem. Driven by surging diagnostic demands, increasing complexity, and growing workforce shortages, the modern laboratory must leverage its generated 'data deluge' to maintain operational excellence and maximize clinical impact.

This presentation explores the strategic evolution of our field by drawing powerful parallels with Industry 4.0. We will examine how concepts such as predictive maintenance and advanced process control can be translated to the medical laboratory. Central to this transformation is the logistical optimization demonstrated in Dr. Eline Tsai’s doctoral research, which utilizes process mining to unravel workflow bottlenecks and machine learning to accurately predict Turnaround Times (TAT).

Furthermore, the session bridges the gap between logistical efficiency and clinical utility. We will highlight the critical transition from purely predictive algorithms to 'Actionable AI'—enabling Clinical Decision Support Systems (CDSS), autoverification, and dynamic triage right at the bedside. Finally, we outline the future of Integrated Diagnostics, where breaking down the silos between clinical chemistry, radiology, and pathology will forge a comprehensive, predictive disease model. Ultimately, mastering our data is no longer just an IT challenge; it is synonymous with delivering superior, proactive patient care.

Beyond the bench: towards AI-driven regional diagnostic networks - prof. dr. Robert de Jonge

In recent years, laboratory automation and process optimization have largely taken place within individual laboratory domains such as Medical Microbiology and Infection Prevention, Human Genetics, Pathology, and Laboratory Medicine. These efforts have delivered higher quality, improved efficiency, and increased capacity.

However, several emerging trends like horizontal and vertical integration, shifting patient distribution, further digitalization, the rise of AI, and the expansion of home‑based care, are set to transform the diagnostic landscape profoundly.

To remain future‑proof, laboratories must look beyond the traditional bench and organize themselves into regional, AI‑enabled diagnostic ecosystems. Achieving this will require a fundamentally new perspective on process improvement, collaboration, and data‑driven diagnostic workflows.

Data-Driven Optimisation of Microbiology Laboratory Workflows - dr. Nashwan Al Naiemi

Clinical microbiology laboratories must process samples efficiently while maintaining high quality and reliability. Automated systems can improve throughput and turnaround times, but their interaction with manual laboratory activities may create complex workflows and hidden bottlenecks. At Labmicta, we study microbiology sample workflows and processing pathways from a laboratory perspective, with a particular interest in understanding workflow interactions, identifying bottlenecks, and improving laboratory efficiency. This work aims to provide insight into operational challenges and opportunities for workflow optimisation, turnaround time improvement, and better resource utilisation within microbiology laboratories.

Optimizing workflow dynamics in modern microbiology laboratories - Negar Abedini

Modern microbiology laboratories combine automated systems with manual processing activities, creating complex workflows that can affect turnaround times and laboratory efficiency. In this work, we study the interaction between automated and manual activities within the microbiology laboratory workflow at Labmicta using laboratory timestamp data. The analysis focuses on loading, imaging, unloading, and technician-related activities to better understand workflow behaviour, waiting times, and operational challenges. The goal of this work is to gain insight into workflow dynamics and bottlenecks, and to explore mathematical and data-driven approaches that can support future optimisation of laboratory operations and resource utilization.