On July 25, 2025, the second meeting of the Laboratory Excellence working group took place at the PULS Technology Center (TZ) in Dingolfing – with a significantly larger group of participants: almost twice as many clinical laboratory representatives from all over Lower Bavaria came together this time to discuss benchmarking, key figures and artificial intelligence (AI) in everyday laboratory work.

Welcome and thematic classification

Prof. Dr.-Ing. Sven Roeren, Dean of the Faculty of Health, Communication and Human-Technology Interaction at Landshut University of Applied Sciences, opened the meeting with a round of introductions and underlined the interdisciplinary nature of the working group. The focus was on the question of how the excellence and effectiveness of clinical laboratories can be measured.

Profitability and benchmarking – status quo and new approaches

The event kicked off with a keynote speech by Roche on the topic of “Laboratory benchmarks and key figures for measuring the efficiency of hospital laboratories”. Despite the existence of models, it became clear that there is a lack of meaningful key figures that can illustrate efficiency and profitability equally and comparably.

Alternative paths to measurability

A new approach was presented by Dr.-Ing Thorsten Pflamm-Jonas, Head of the Health business unit at roeren, which is being developed as part of Andrea Wagensoner’s final thesis at Landshut University of Applied Sciences. Based on qualitative interviews with laboratory managers, an evaluation matrix is being developed that goes beyond traditional key figures. The results will be presented this year at the German Congress for Laboratory Medicine in Leipzig. The working group considered this methodological approach to be particularly valuable and forward-looking.

Artificial intelligence in the health sector

In the third thematic block, Dr. Andreas Hofer, Managing Director of Aperion Analytics GmbH, addressed the highly topical subject of “AI in healthcare”. The focus was on the structured development of understanding and clear definition of terms, as well as practical classification for everyday laboratory work. Specific use cases ranged from the visualization of complex data patterns and prognosis calculations for laboratory processes and disease progression to ideas for the use of AI in quality control and resource planning.

“AI in healthcare is highly exciting and has enormous potential – this was evident in the working group. This is precisely the aim of the working group: to make concrete approaches for everyday laboratory work visible and to think ahead,” says Dr. Thorsten Pflamm-Jonas.

The working group enters the next round

In the concluding discussion, it became clear once again how important suitable key figures are for assessing the performance of laboratories. Sven Roeren emphasized that it is not always helpful to use uninterpreted data from financial systems, as many specific characteristics of laboratory areas are not taken into account. There was a consensus that comparability between individual laboratories is massively impaired. Also, medically meaningful collateral benefits, such as specific development trends of resistance to antibiotics, are rarely included at all in the performance evaluation of laboratories. A third meeting of the working group is expected to take place in November – this time directly in a clinical laboratory in order to further strengthen the practical relevance.