Adrien Wartelle

Les phénomènes d’engorgement sont des problématiques majeures dans la gestion de la tension des systèmes de soins pour la prise en charge adéquate de la santé populationnelle à toutes les échelles. Dans ce cadre, cette thèse montre une démarche générale et généralisable de modélisation et d’évaluation de l’engorgement des systèmes, en particulier du système de santé avec une application aux services d’urgences. La quantification de l’engorgement constitue le premier point adressé à travers la caractérisation des processus d’arrivée et de départ d’un système. Une nouvelle mesure de congestion sur un ratio entre charge d’arrivée et de départ est alors introduite puis comparée à d’autres mesures comme l’occupation ou le temps d’attente moyen. La modélisation de cette congestion est ensuite étudiée par une approche de file d’attente axée sur les données du système réel. L’ensemble des processus stochastiques d’arrivée, de transitions et de départs de stations avec la variable centrale de probabilité de départ instantané des patients, est alors modélisé selon différentes méthodes d’estimation. Le modèle alors conçu est appliqué dans une étude de simulation de l’impact d’ouverture de cliniques sur la congestion des urgences où le flux patient impacté est identifié à l’aide d’une méthode de clustering et d’étude temporelle. Par une démarche de recherche opérationnelle fondée sur les données, cette thèse offre alors de nouvelles opportunités d’étude comportementale, de prévision et d’optimisation de la congestion.

Objectives: Emergency department crowding refers to a complex state of congestion associated with a set of performance indicators such as occupation levels, waiting times and specific scores. Among current methods to model it, an objective gap exists between forecasting machine learning methods, focusing on prediction precision and queueing and simulation methods, focusing on capturing correctly the effect of decision variables for evaluation and optimisation purposes. The objective of the present analysis is to implement and numerically validate a novel data-driven queueing methodology that can bridge this gap and to show its applicability in a simulation case study. Methods: A statistical modelling of the queueing processes, particularly patient departure rates and probabilities, is developed to cross the gap defined above. Using the data from a major emergency department of eastern France, the resultant data-driven queueing network model is validated and applied through a synchronous simulation algorithm. Results: The model obtained considers the complex effects of patient arrivals and doctor and nurse allocations while offering an unbiased and accurate measure of long-term crowding. Its application with the case study quantifies the impact of the opening of new Unscheduled Care Services on emergency department crowding. Discussion: The new data-driven queueing methodology is able to model and quantify complex crowding effects at a detailed level in an emergency department. Conclusions: This study shows an alternative approach successfully bridging the modelling gap by establishing a model that can effectively predict system crowding dynamics under the influence of multiple key variables.

Background: In France, the number of emergency department (ED) admissions doubled between 1996 and 2016. To cope with the resulting crowding situation, redirecting patients to new healthcare services was considered a viable solution which would spread demand more evenly across available healthcare delivery points and render care more efficient. The objective of this study was to analyze the impact of opening new on-demand care services based on variations in patient flow at a large hospital emergency department. Methods: We performed a before-and-after study investigating the use of unscheduled care services in the Aube region in eastern France, that focused on ED attendance at Troyes Hospital. A hierarchical clustering based on co-occurrence of diagnoses was applied which divided the population into different multimorbidity profiles. Temporal trends of the resultant clusters were also studied empirically and using regression models. A multivariate logistic regression model was constructed to adjust the periodic effect for appropriate confounders and therefore confirm its presence. Results: In total, 120,722 visits to the ED were recorded over a 24-month period (2018–2019) and 16 clusters were identified, accounting for 94.76% of all visits. There was a decrease of 56.77 visits per week in seven specific clusters and an increase of use of unscheduled health care services by 328.12 visits per week. Conclusions: Using an innovative and reliable methodology to evaluate changes in patient flow through the ED, these findings may help inform public health policy experts on the implementation of unscheduled care services to ease pressure on hospital EDs.

Assessing the health profiles of populations is a crucial task to create a coherent healthcare offer. Emergency Departments (EDs) are at the core of the healthcare system and could benefit from this evaluation via an improved understanding of the healthcare needs of their population. This paper proposes a novel hierarchical agglomerative clustering algorithm based on multimorbidity analysis. The proposed approach constructs the clustering dendrogram by introducing new quality indicators based on the relative risk of co-occurrences of patient diagnoses. This algorithm enables the detection of multimorbidity patterns by merging similar patient profiles according to their common diagnoses. The multimorbidity approach has been applied to the data of the largest ED of the Aube Department (Eastern France) to cluster its patient visits. Among the 120,718 visits identified during a 24-month period, 16 clusters were identified, accounting for 94.8% of the visits, with the five most prevalent clusters representing 63.0% of them. The new quality indicators show a coherent and good clustering solution with a cluster membership of 1.81 based on a cluster compactness of 1.40 and a cluster separation of 0.77. Compared to the literature, the proposed approach is appropriate for the discovery of multimorbidity patterns and could help to develop better clustering algorithms for more diverse healthcare datasets.

Background: In France, the number of admissions to emergency departments doubled between 1996 and 2016, leading to overcrowding. To cope with the resultant overcrowding, redirecting patients to new healthcare services is a viable solution, to spread demand more evenly across available healthcare delivery points, and render care more efficient. The goal of this study was to analyse the impact of opening new unscheduled care services on variations in patient attendance at a large emergency department. Methods: We performed a before-and-after study investigating the use of unscheduled care services in the Aube Department (Eastern France), focusing on emergency department attendance of Troyes Hospital. We applied a hierarchical clustering based on co-occurrence of diagnoses, to divide the population into different multimorbidity profiles and study their temporal trends. A multivariate logistic regression model was constructed to adjust the period effect for appropriate confounders. Results: In total, 120,718 visits to the emergency department were recorded over a 24-month period (2018-2019), and 14 clusters were identified accounting for 94.76% of all visits. The before-and-after analysis showed a decrease of 57.95 visits per week in 7 specific clusters, while the consumption of unscheduled health care services increased by 328.12 visits per week.Conclusions: Using an innovative and reliable methodology to evaluate changes in patient flow through the emergency department, our results could help to inform public health policy regarding the implementation of unscheduled care services, to ease pressure on emergency departments.