The ELODI Industrial Chair (“E-Logistics and Digitalisation of Therapeutic Management in Health Facilities“) aims to build a centre of technological innovation in the field of Pharmacy around digital, human, social and medical sciences. The aim is to optimise the therapeutic management of inpatients. It addresses important issues for the area in the health and digital sectors. To achieve this, it has a total budget of €1.83 million, including support from the European Metropolis of Lille (MEL) and the I-SITE ULNE granted as part of the MEL call for “industrial chairs” projects.
These include research programmes and demonstrators to capitalise on the know-how in the production and logistics of medicines, as well as artificial intelligence applied to the field of the distribution of these health products. The work will also develop a training offer (initial and continuous) to meet the needs of the socio-economic players in health logistics. The Chair will also contribute to the networking of academic and socio-economic participants. Finally, workshops and experiments will take place.
Led by the Pharmacy of the University Hospital of Lille, alongside Centrale Lille, the University of Lille and the Altao, Mates, Ascent and Computer Engineering companies, the ELODI chair is based particularly on the research work of the CRIStAL laboratory. The OSL team, led by Professor Slim Hammadi, will work specifically on the alliance between AI and the optimisation to improve healthcare logistics systems. Their work will contribute in this context to the development of the know-how in logistics and artificial intelligence to optimise the health products circuit.
Centrale Lille is already collaborating with Computer Engineering and Altao on work to introduce augmented reality into the production stage of drugs, including high-risk drugs such as experimental drugs and the development of a medical decision support system to predict TB disease.
As part of the ELODI Chair, they will continue this collaboration to design and develop AI algorithms that will enable the detection, prevention and management of high-risk iatrogenic situations.