OBJECTIVES: Within the French Genomics Medicine Plan (PFMG-2025), two multi-site medical genomic laboratories (LBMMS) have been launched to develop high-throughput genomic sequencing. These two LBMMS, called AURAGEN and SeqOIA, are each composed on a sequencing facility, calculation, and archiving infrastructures. An operational chain of genomic sequencing configuration and supervision makes possible to manage targeted patients for rare diseases and cancers. The aim of this study is to develop a digital twin which can be used as an organizational analysis tool, to support decision-making help to improve the performance of the AURAGEN platform.

METHODS: A hybrid Discrete-event simulation (DES) and Agent-Based Modelling simulation (ABM) model was developed. Patients were modelled as agents, and their medical genomic pathways represented using flowcharts. A second agent was created to model the operations on the platform. The first step towards building the model consisted in mapping the different steps of the process, identifying the actors and resources involved at each steps and linking the activities to each other. This representation was built in co-construction with key operational actors of the process. The model was tested and validated. Sensitivity analyses were performed.

RESULTS: At a level of 200 samples for rare diseases and 10 for cancers per week, mean lead time was 45 days. Laboratory technician’s occupancy rate reached 70% with significant variations between operations, 53% for genotyping, 85% for the reception of samples, and even 91% for normalization and sequencing. A 20% increase in samples would increase the number of completed prescriptions by 6%, and mean lead times by 117%. Technician occupancy rate would reach 94% for normalization and sequencing.

CONCLUSIONS: Digital twin provides a decision-help to specify the total product function, to identify bottlenecks and additional resource requirements, to assess the impact of production planning modifications on performance indicators, and finally to achieve technical efficiency.