Augmented Reality Eyewear for Assisting in the Preparation and Control of Cancer Therapies: Assessing the Learning Curve and Level of Adoption.

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.

A prospective, single-centre study of the feasibility of the use of augmented reality for improving the safety and traceability of injectable investigational cancer drug compounding.

The compounding of injectable cancer drugs for clinical trials often requires specific procedures, with limited access to the starting materials and especially the active compound. These characteristics prevent the application of qualitative or quantitative analyses and quality control techniques. Hence, for some very complex compounding operations, double visual inspection is considered to be less reliable, more time-consuming and more human-resource-intensive than other methods.

Pharmaceutical Decision Support System Using Machine Learning to Analyze and Limit Drug-Related Problems in Hospitals.

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.

Symbolic Artificial Intelligence to Diagnose Tuberculosis Using Ontology.

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.

Ontology for Overcrowding Management in Emergency Department.

Emergency department (ED) overcrowding is an ongoing problem worldwide. Scoring systems are available for the detection of this problem. This study aims to combine a model that allows the detection and management of overcrowding.

Impact of the Automation of Inpatient Bed Management to Reduce the Emergency Service Waiting Time.

The patient waiting time to be transferred for hospitalization is the time that the patient waits between the decision to hospitalize and the actual admission to an inpatient hospital bed. One of the difficulties encountered in qualifying waiting time for inpatient bed is the inability of hospital information systems to measure it. Hospitals in France have a specialized bed allocation team.

Introducing Augmented Reality Technique to Enhance the Preparation Circuit of Injectable Chemotherapy Drugs.

Chemotherapy preparations are often complex and subject to a strict regulatory context. The existing control methods are often limited to Double Visual Control (DVC). In this paper, the preparation circuit of chemotherapy drugs is evaluated through data collection and statistical analysis in order to highlight the difficulties encountered.

An Innovative Approach to Jointly Scheduling and Assigning a Consultation Time to Patients Arriving in the Emergency Department.

Emergency departments (ED) are facing problems related to the growing demand of care. Patients' management is carried out according to the type of patient and care required: already scheduled patients and non-scheduled urgent and non-urgent patients arriving in the ED. One of the main problems confronted in hospitals is the permanent interference between these different types of patients to be treated under the stochastic behaviors of consultation time and arrival flows, which prevents any prior planning.

Agents endowed with uncertainty management behaviors to solve a multiskill healthcare task scheduling.

Health organizations are complex to manage due to their dynamic processes and distributed hospital organization. It is therefore necessary for healthcare institutions to focus on this issue to deal with patients' requirements. We aim in this paper to develop and implement a management decision support system (DSS) that can help physicians to better manage their organization and anticipate the feature of overcrowding.

Health Care Decision Support System for the Pediatric Emeregency Department Management.

Health organization management is facing a high amount of complexity due to the inherent dynamics of the processes and the distributed organization of hospitals. It is therefore necessary for health care institutions to focus on this issue in order to deal with patients' requirements and satisfy their needs. The main objective of this study is to develop and implement a Decision Support System which can help physicians to better manage their organization, to anticipate the overcrowding feature, and to establish avoidance proposals for it.

Multi-agent Architecture for the Multi-Skill Tasks Modeling at the Pediatric Emergency Department.

Patient journey in the Pediatric Emergency Department is a highly complex process. Current approaches for modeling are insufficient because they either focus only on the single ancillary units, or therefore do not consider the entire treatment process of the patients, or they do not account for the dynamics of the patient journey modeling. Therefore, we propose an agent based approach in which patients and emergency department human resources are represented as autonomous agents who are able to react flexible to changes and disturbances through pro-activeness and reactiveness.

Mapping patient path in the Pediatric Emergency Department: A workflow model driven approach.

The workflow models of the patient journey in a Pediatric Emergency Department (PED) seems to be an effective approach to develop an accurate and complete representation of the PED processes. This model can drive the collection of comprehensive quantitative and qualitative service delivery and patient treatment data as an evidence base for the PED service planning. Our objective in this study is to identify crowded situation indicators and bottlenecks that contribute to over-crowding.

A workflow model to analyse pediatric emergency overcrowding.

The greatest source of delay in patient flow is the waiting time from the health care request, and especially the bed request to exit from the Pediatric Emergency Department (PED) for hospital admission. It represents 70% of the time that these patients occupied in the PED waiting rooms. Our objective in this study is to identify tension indicators and bottlenecks that contribute to overcrowding.