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. 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.

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. The compounding team at Lille University Hospital (Lille, France) has equipped one of its preparation areas with a new device: augmented reality (AR) eyewear connected to an oncology drug management system, as a support tool for compounding and quality control. The tool has been tested, adapted and improved within the unit and is now used for investigational drug compounding on a routine basis. In a prospective, single-centre study, we evaluated the feasibility of the implementation of this novel AR approach for the compounding of injectable investigational cancer drugs. During the 6-month study period, 564 clinical trial compounding operations were performed with the AR eyewear. The proportion of poor-quality photos taken with the AR eyewear fell over time, as users became more familiar with the tool. A user satisfaction survey highlighted a very high level of uptake and a wish to broaden the scope of the compounding performed with AR support. The AR eyewear constitutes an innovative, cost-effective tool that increased the level of safety without disrupting the unit's operating procedures. The tool's flexibility enabled its integration into a variety of working environments. The various improvements now being developed should help to further boost the added value of this novel device.

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. 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.

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. 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.

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. The results regarding preparation and control times and the number of task interruptions highlight the unreliability of the DVC and its impact on processing time. As a solution, we propose a decision support system "Smart Prep" based on Augmented Reality (AR), co-developed, and commercialized by the Faculty of Pharmacy of Lille, Ecole Centrale de Lille and the company Computer Engineering. This system allows the preparation of chemotherapy drugs according to a step-by-step mode, a traceability of the preparation steps and a reduction of tasks' interruptions.

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. This team must manage the bed allocation problem between different hospital departments using phone communication to assign patients to the adapted service. This kind of communication represents a lengthy additional workload in which effectiveness is uncertain. This paper presents a new approach to automate bed management in downstream service. For that, we have implemented algorithms based on artificial intelligent integrated in an inpatient web platform using IoT-Beacons, which is implemented to improve and facilitate the exchange of availability information of downstream beds within the Lille university hospital center (LUHC).

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. Therefore, it is crucial to implement a system that can reason model, rank ED resources and ED performance indicators based on environmental factors. Thus, we propose in this paper a new domain ontology (EDOMO) based on a new overcrowding estimation score (OES) to detect critical situations, specify the level of overcrowding and propose solutions to deal with these situations. Our approach is based on a real database created during more than four years from the Lille University Hospital Center (LUHC) in France. The resulting ontology is capable of modeling complete domain knowledge to enable semantic reasoning based on SWRL rules. The evaluation results show that the EDOMO is complete that can enhance the functioning of the ED.

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. The present work proposes a dynamic scheduling method, considering the impact of new patients' arrivals on the treatment of patients already scheduled to minimize the mean waiting time of patients in the ED. The originality of this work is to assign, at the time of arrival, a scheduled time to each patient in order to reduce their stress. The performance of the proposed method is examined through a concrete application in the Pediatric Emergency Department of CHRU of Lille.

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. Our objective is to optimize the Pediatric Emergency Department (PED) functioning characterized by stochastic arrivals of patients leading to its services overload. Human resources allocation presents additional complexity related to their different levels of skills and uncertain availability dates. So, we propose a new approach for multi-healthcare task scheduling based on a dynamic multi-agent system. Decisions about assignment and scheduling are the result of a cooperation and negotiation between agents with different behaviors. We therefore define the actors involved in the agents' coalition to manage uncertainties related to the scheduling problem and we detail their behaviors. Agents have the same goal, which is to enhance care quality and minimize long waiting times while respecting degrees of emergency. Different visits to the PED services and regular meetings with the medical staff allowed us to model the PED architecture and identify the characteristics and different roles of the healthcare providers and the diverse aspects of the PED activities. Our approach is integrated in a DSS for the management of the Regional University Hospital Center (RUHC) of Lille (France). Our survey is included in the French National Research Agency (ANR) project HOST (Hôpital: Optimisation, Simulation et évitement des Tensions (ANR-11-TecSan-010: http://host.ec-lille.fr/wp-content/themes/twentyeleven/docsANR/R0/HOST-WP0.pdf)).

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. This work is a part of HOST project (Hospital: Optimization, Simulation, and Crowding Avoidance) of the French National Research Agency (ANR). It aims to optimize the functioning of the Pediatric Emergency Department characterized by stochastic arrivals of patients which leads to its overcrowding and services overload. Our study is a set of tools to smooth out patient flows, enhance care quality and minimize long waiting times and costs due to resources allocation. So we defined a decision aided tool based on Multi-agent Systems where actors negotiate and cooperate under some constraints in a dynamic environment. These entities which can be either physical agents representing real actors in the health care institution or software agents allowing the implementation of optimizing tools, cooperate to satisfy the demands of patients while respecting emergency degrees. This paper is concerned with agents' negotiation. It proposes a new approach for multi-skill tasks scheduling based on interactions between agents.

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. The main aim of this paper is to present the overall design of the proposed multi-agent system, emphasizing its architecture and the behavior of each agent of the model. Besides, we describe inter-agent communication based on the agent interaction protocol to ensure cooperation between agents when they perform the coordination of tasks for the users. This work is integrated into the ANR HOST project (ANR-11-TecSan-010).

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. 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 PED for hospital admission. It represented 70% of the time that these patients occupied in the PED waiting rooms. The use of real data to construct the workflow model of the patient path is effective in identifying sources of delay in patient flow, and aspects of the PED activity that could be improved. The development of this model was based on accurate visits made in the PED of the Regional University Hospital Center (CHRU) of Lille (France). This modeling, which has to represent most faithfully possible the reality of the PED of CHRU of Lille, is necessary. It must be detailed enough to produce an analysis allowing to identify the dysfunctions of the PED and also to propose and to estimate prevention indicators of crowded situations. Our survey is integrated into the French National Research Agency (ANR) project, titled: "Hospital: Optimization, Simulation and avoidance of strain" (HOST).

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. Patient flow mapping through the PED was carried out in a continuous 2 years period from January 2011 to December 2012. Our method is to use the collected real data, basing on accurate visits made in the PED of the Regional University Hospital Center (CHRU) of Lille (France), in order to construct an accurate and complete representation of the PED processes. The result of this representation is a Workflow model of the patient journey in the PED representing most faithfully possible the reality of the PED of CHRU of Lille. This model allowed us to identify sources of delay in patient flow and aspects of the PED activity that could be improved. It must be enough retailed to produce an analysis allowing to identify the dysfunctions of the PED and also to propose and to estimate prevention indicators of tensions. Our survey is integrated into the French National Research Agency project, titled: "Hospital: optimization, simulation and avoidance of strain" (ANR HOST).