Slim Hammadi

Patient care involves multiple healthcare professionals and carries inherent risks. Addressing human factors is essential to mitigating these risks and improving professional practices. Cognitive aids are one strategy to standardize tasks based on clinical guidelines. A multidisciplinary team, consisting of anesthesiologists, pharmacists, ergonomists, and engineers, aimed to design an innovative device to facilitate cognitive aid usage. The initial phase of the project involved evaluating the usability and utility of a prototype: augmented reality eyewear controlled by voice commands. Usability was assessed using the System Usability Scale (SUS), alongside semi-structured interviews conducted between July and August. A pharmacy resident conducted 13 interviews with anesthesiologists recruited from three hospitals within the Lille University Hospital Center, using a nine-question guide developed with an ergonomist. Interviews averaged 35.9 minutes (±10.4), and the average SUS score was 72.5 (±15.6). The prototype was generally well received, with participants suggesting the integration of cognitive aids for emergency situations. However, despite positive feedback on the concept, the format of the prototype was not favored. Ergonomic feedback will guide further device improvements, and additional research on technology adoption is required.

Since March 2022, the centralized cytotoxic preparation unit at the Lille University Hospital (Lille, France) is equipped with augmented reality eyewear for preparation and quality control. The technology enables a user-friendly guided step by step preparation process. It also assists the user by identifying vials through data matrix scan and recording photos at different stages of preparation in order to replace the in-process double visual inspection which will now be carried out a posteriori during the release control. In this paper, we evaluate user feedback and model the learning curve for this new tool. The team’s feedback was evaluated using the System Usability Scale (SUS) and Short User Experience Questionnaire (S-UEQ). Both questionnaires showed very good acceptance of the tool by our teams, with scores of 79.7 for the SUS and 2.014 for the UEQ. Finally, a learning curve was drawn up according to Wright, showing a learning curve of 91%. This study shows that the tool has been very well integrated into our preparation unit.

The health product circuit corresponds to the chain of steps that a medicine goes through in hospital, from prescription to administration. The safety and regulation of all the stages of this circuit are major issues to ensure the safety and protect the well-being of hospitalized patients. In this paper we present an automatic system for analyzing prescriptions using Artificial Intelligence (AI) and Machine Learning (ML), with the aim of ensuring patient safety by limiting the risk of prescription errors or drug iatrogeny. Our study is made in collaboration with Lille University Hospital (LUH). We exploited the MIMIC-III (Medical Information Mart for Intensive Care) a large, single-center database containing information corresponding to patients admitted to critical care units at a large tertiary care hospital.

Pulmonary Tuberculosis (PTB) is an infectious disease caused by a bacterium called Mycobacterium tuberculosis. This paper aims to create Symbolic Artificial Intelligence (SAI) system to diagnose PTB using clinical and paraclinical data. Usually, the automatic PTB diagnosis is based on either microbiological tests or lung X-rays. It is challenging to identify PTB accurately due to similarities with other diseases in the lungs. X-ray alone is not sufficient to diagnose PTB. Therefore, it is crucial to implement a system that can diagnose based on all paraclinical data. Thus, we propose in this paper a new PTB ontology that stores all paraclinical tests and clinical symptoms. Our SAI system includes domain ontology and a knowledge base with performance indicators and proposes a solution to diagnose current and future PTB also abnormal patients. Our approach is based on a real database of more than four years from our collaborators at Pondicherry hospital in India.

We present an advanced traveler information system (ATIS) for public and private transportation, including vehicle sharing and pooling services. The ATIS uses an agent-based architecture and multi-objective optimization to answer trip planning requests from multiple users in a co-modal setting, considering vehicle preferences and conflicting criteria. At each set of users' requests, the transportation network is represented by a co-modal graph that allows decomposing the trip planning problem into smaller tasks: the shortest routes between the network nodes are determined and then combined to obtain possible itineraries. Using multi-objective optimization, the set of user-vehicle-route combinations according to the users' preferences is determined, ranking all possible route agents' coalitions. The ATIS is tested for the real case study of the Lille metropolitan area (Nord Pas de Calais, France).