Improving operational effectiveness of tactical master plans for emergency and elective patients under stochastic demand and capacitated resources

This paper develops a two-stage planning procedure for master planning of elective and emergency patients while allocating at best the available hospital resources. Four types of resources are considered: operating theatre, beds in the medium and in the intensive care units, and nursing hours in the intensive care unit. A tactical plan is obtained by minimizing the deviations of the resources consumption to the target levels of resources utilization. Some capacity is reserved for emergency care. To deal with the deviation between actually arriving patients and the average number of patients on which the tactical plan is based, we consider the option of planning a higher number of patients (overplanning). To adapt the tactical plan to the actual stream of elective patients, we also consider flexibility rules. Overplanning and flexibility leads to a weekly schedule of elective patients. This schedule is modified to account for emergency patients. Scheduled elective patients may be cancelled and emergency patients may be sent to other hospitals. Cancellations rules for both types of patients rely on the possibility to exceed the available capacities. Several performance indicators are defined to assess patient service/dissatisfaction and hospital efficiency. Simulation results show a trade-off between hospital efficiency and patient service. We also obtain a rank of the different strategies: overplanning, flexibility and cancellation rules

A multilevel integrative approach to hospital case mix and capacity planning.

Hospital case mix and capacity planning involves the decision making both on patient volumes that can be taken care of at a hospital and on resource requirements and capacity management. In this research, to advance both the hospital resource efficiency and the health care service level, a multilevel integrative approach to the planning problem is proposed on the basis of mathematical programming modeling and simulation analysis. It consists of three stages, namely the case mix planning phase, the master surgery scheduling phase and the operational performance evaluation phase. At the case mix planning phase, a hospital is assumed to choose the optimal patient mix and volume that can bring the maximum overall financial contribution under the given resource capacity. Then, in order to improve the patient service level potentially, the total expected bed shortage due to the variable length of stay of patients is minimized through reallocating the bed capacity and building balanced master surgery schedules at the master surgery scheduling phase. After that, the performance evaluation is carried out at the operational stage through simulation analysis, and a few effective operational policies are suggested and analyzed to enhance the trade-offs between resource efficiency and service level. The three stages are interacting and are combined in an iterative way to make sound decisions both on the patient case mix and on the resource allocation.Health care; Case mix and capacity planning; Master surgery schedule; Multilevel; Resource efficiency; Service level;

Stochastic surgery selection and sequencing under dynamic emergency break-ins

Anticipating the impact of urgent emergency arrivals on operating room schedules remains methodologically and computationally challenging. This paper investigates a model for surgery scheduling, in which both surgery durations and emergency patient arrivals are stochastic. When an emergency patient arrives he enters the first available room. Given the sets of surgeries available to each operating room for that day, as well as the distributions of the main stochastic variables, we aim to find the per-room surgery sequences that minimise a joint objective, which includes over- and under-utilisation, the amount of cancelled patients, as well as the risk that emergencies suffer an excessively long waiting time. We show that a detailed analysis of emergency break-ins and their disruption of the schedule leads to a lower total cost compared to less sophisticated models. We also map the trade-off between the threshold for excessive waiting time, and the set of other objectives. Finally, an efficient heuristic is proposed to accurately estimate the value of a solution with significantly less computational effort.Anticipating the impact of urgent emergency arrivals on operating room schedules remains methodologically and computationally challenging. This paper investigates a model for surgery scheduling, in which both surgery durations and emergency patient arrivals are stochastic. When an emergency patient arrives he enters the first available room. Given the sets of surgeries available to each operating room for that day, as well as the distributions of the main stochastic variables, we aim to find the per-room surgery sequences that minimise a joint objective, which includes over- and under-utilisation, the amount of cancelled patients, as well as the risk that emergencies suffer an excessively long waiting time. We show that a detailed analysis of emergency break-ins and their disruption of the schedule leads to a lower total cost compared to less sophisticated models. We also map the trade-off between the threshold for excessive waiting time, and the set of other objectives. Finally, an efficient heuristic is proposed to accurately estimate the value of a solution with significantly less computational effort.A

Improve OR-schedule to reduce number of required beds

After surgery most of the surgical patients have to be admitted in a ward in the hospital. Due to financial reasons and an decreasing number of available nurses in the Netherlands over the years, it is important to reduce the bed usage as much as possible. One possible way to achieve this is to create an operating room (OR) schedule that spreads the usage of beds nicely over time, and thereby minimizes the number of required beds. An OR-schedule is given by an assignment of OR-blocks to specific days in the planning horizon and has to fulfill several resource constraints. Due to the stochastic nature of the length of stay of patients, the analytic calculation of the number of required beds for a given OR-schedule is a complex task involving the convolution of discrete distributions. In this paper, two approaches to deal with this complexity are presented. First, a heuristic approach based on local search is given, which takes into account the detailed formulation of the objective. A second approach reduces the complexity by simplifying the objective function. This allows modeling and solving the resulting problem as an ILP. Both approaches are tested on data provided by Hagaziekenhuis in the Netherlands. Furthermore, several what-if scenarios are evaluated. The computational results show that the approach that uses the simplified objective function provides better solutions to the original problem. By using this approach, the number of required beds for the considered instance of HagaZiekenhuis can be reduced by almost 20%

Balancing operating theatre and bed capacity in a cardiothoracic centre

Cardiothoracic surgery requires many expensive resources. This paper examines the balance between operating theatres and beds in a specialist facility providing elective heart and lung surgery. Without both operating theatre time and an Intensive Care bed a patient's surgery has to be postponed. While admissions can be managed, there are significant stochastic features, notably the cancellation of theatre procedures and patients' length of stay on the Intensive Care Unit. A simulation was developed, with clinical and management staff, to explore the interdependencies of resource availabilities and the daily demand. The model was used to examine options for expanding the capacity of the whole facility. Ideally the bed and theatre capacity should be well balanced but unmatched increases in either resource can still be beneficial. The study provides an example of a capacity planning problem in which there is uncertainty in the demand for two symbiotic resources

Improving probability distributions for resource levels from master surgery tactical plans for emergency and elective patients

Efficiency and patient satisfaction are two of the most important factors for a hospital; in order to be competitive these two factors have to be improved. Tactical admission plans are focused on increasing efficiency, but in this paper we try to also associate patient satisfaction with the tactical plan. To this respect, we present a procedure to calculate exact waiting time distributions and another procedure to compute the exact level of resources usage. Then we explore two different methods to improve tactical plans to avoid overuse of IC beds, we consider this as the most critical resource: cancellation of emergency patients operations and cancellation of elective patients operations. Data from a Dutch cardiothoracic surgery center is used to base our case study on and shows that the cancelation of operation leads to an increase of hospital efficiency but it can have some negative effects in the patient satisfaction.Outgoin

Integrative Predictive Support Systems for Hospital’s Resource Planning and Scheduling

RÉSUMÉ: Le système de santé du Canada a du mal à gérer le nombre croissant de patients ayant plusieurs maladies chroniques nécessitant l’accès à des soins de longue durée, et cela, principalement en raison de vieillissement de la population. Cela entraîne notamment de longs délais d’attente pour les patients et une augmentation des frais des soins de santé. Comme les hôpitaux représentent la plus grande part du budget de la santé, ils doivent améliorer leur efficacité opérationnelle en utilisant plus efficacement leurs ressources. En particulier, les hôpitaux qui fournissent aux patients des soins directs et un accès à des ressources coûteuses telles que les chirurgiens, les salles d’opération, les unités de soins intensifs et les salles d’opération, ont de la pression pour gérer leurs ressources efficacement. Les chercheurs en recherche opérationnelle ont largement abordé les problèmes liés à la planification et l’ordonnancement des ressources dans les hôpitaux pendant de nombreuses années. Les modèles analytiques conventionnels visent ainsi à améliorer l’efficacité de la prise de décision de planification des ressources hospitalières à des fins stratégiques (à long terme), tactiques (à moyen terme) et opérationnelles (à court terme). Cependant, ces modèles ont du mal à adresser efficacement la complexité, la variabilité, et l’incertitude inhérentes aux opérations hospitalières, car ils utilisent souvent des distributions statistiques simplistes pour émuler ces opérations. Par conséquent, ils sont sous-optimaux dans des contextes réels d’utilisation. Avec l’accroissement continu des quantités massives de données collectées dans les hôpitaux et les systèmes de santé, ainsi que les progrès dans le domaine de la modélisation prédictive, la communauté de la recherche opérationnelle a maintenant l’occasion de mieux analyser, comprendre et reproduire la complexité, la variabilité et l’incertitude des opérations hospitalières. À cette fin, l’objectif principal de cette thèse est de développer des cadres prédictifs intégrés, capables d’analyser et d’extraire des informations à partir de masses de données afin de mieux éclairer la planification et l’ordonnancement des ressources hospitalières aux niveaux stratégique, tactique, et opérationnel. Au meilleur des connaissances de l’auteur, cette thèse est une des premières à proposer des cadres pour la conception de systèmes prédictifs dans les hôpitaux. Au niveau stratégique et tactique, le premier article (chapitre 4) développe un cadre hybride basé sur l’apprentissage machine et la simulation pour prédire la demande personnalisée des patients au niveau des ressources hospitalières. Le cadre reflète notamment la relation à long terme entre les hôpitaux et les patients ayant des maladies chroniques, couvrant ainsi un horizon à long terme et intégrant le fait que les patients ont besoin, non pas d’une, mais de plusieurs visites à l’hôpital et accès à divers types de ressources. Dans cette thèse, nous proposons une approche novatrice basée sur l’apprentissage profond avec notamment un modèle de réseaux de neurones qui modélise les interactions complexes des patients chroniques avec les ressources hospitalières tout au long de leurs trajectoires de traitement. Cette nouvelle approche propose une série de réseaux de neurones où l’entrée de chaque réseau est définie comme la sortie de prédiction de son précédent. Les modèles proposés sont ainsi capables de prédire le traitement suivant du patient avec une précision (« recall ») allant de 68% à 79%. En plus de prévoir la prochaine étape des traitements des patients, nous proposons aussi une deuxième série de réseaux de neurones qui fournissent le temps prévu pour le prochain traitement. Ces trajectoires temporelles ainsi prédites sont ensuite incorporées dans une simulation à base d’agents capable de prédire la demande personnalisée et agrégée en ressources rares des hôpitaux à moyen et long terme en fonctions des profils des patients à traiter. Nous avons appliqué ce cadre intégratif à des données hospitalières réelles et montrons que le cadre proposé prédit efficacement la demande à moyen et à long terme de ressources rares dans les hôpitaux avec une précision de 77% (trajectoire) et de 64% (délai entre étapes), qui surpasse considérablement à la fois les méthodes traditionnelles de prévision demande et les techniques standard d’apprentissage automatique. Au niveau tactique et opérationnel, l’article présenté au chapitre 5 propose un modèle intégratif pour la prédiction des durées d’intervention chirurgicale personnalisées. Ce cadre est le premier de ce genre, et permet d’incorporer des attributs opérationnels et temporels liés à la planification, en plus d’attributs liés aux patients, aux procédures et aux chirurgiens pour prévoir ainsi la durée des interventions chirurgicales. De plus, ce cadre illustre l’efficacité d’algorithmes d’apprentissage automatique, tels que « Random Forest » et « Support Vector Machine » pour capturer les relations complexes entre les prédicteurs de la durée des interventions chirurgicales. Nous avons appliqué ce cadre à des données hospitalières réelles et constaté une amélioration de 31% de la précision des prédictions par rapport à la pratique. De plus, les résultats montrent que les décisions liées à la planification telles que l’ordonnancement des procédures et l’affectation des blocs ont un impact significatif sur les durées d’intervention chirurgicale. Ce résultat a des implications importantes pour la littérature dédiée à la planification et l’ordonnancement des salles d’opération aux niveaux tactique et opérationnel. Autrement dit, ce résultat implique que la planification optimale des salles d’opération n’est possible que si l’on optimise conjointement la durée et l’ordre des chirurgies. Au niveau opérationnel, l’article présenté au chapitre 6 propose un modèle intégratif pour la prédiction du risque de défaillance opérationnelle, et notamment du risque de temps supplémentaire. En pratique, même le plus précis des outils utilisés ne permet pas de prédire la variabilité des processus hospitaliers avec une précision de 100%. Par conséquent, au niveau opérationnel, il est important d’éviter les décisions qui ont un risque élevé d’échec pouvant ainsi entraîner des conséquences négatives significatives, qui peuvent à leur tour impliquer des coûts supplémentaires, une qualité de soins inférieure, et causer une insatisfaction à la fois des patients et du personnel. Dans cette thèse, nous appliquons des techniques d’apprentissage machine probabiliste au problème des heures supplémentaires en salle d’opération. Plus précisément, nous montrons, en utilisant des données hospitalières réelles, que les algorithmes proposés sont capables de classer les horaires des salles d’opération qui entraînent des heures supplémentaires avec une précision de 88%. La performance des prédictions ainsi calculées est de plus améliorée grâce à l’utilisation de techniques d’étalonnage appliquées aux résultats d’algorithmes d’apprentissage automatique. Le modèle de risque proposé a ainsi des implications significatives à la fois pour la pratique de la gestion les ressources au niveau opérationnel, mais aussi pour la littérature académique. Tout d’abord, le modèle de risque proposé peut facilement être intégré dans les systèmes de planification des salles à l’hôpital afin d’aider les décideurs à éviter des horaires risqués. Deuxièmement, le modèle de risque proposé peut être utilisé conjointement avec les modèles existants d’ordonnancement des salles d’opération pour améliorer la performance opérationnelle des solutions.----------ABSTRACT: Canada’s health care system is struggling to manage the increasing demand of patients with multiple chronic issues who require access to long-term care, primarily due to Canada’s aging population. This has resulted in long patient wait times and increasing healthcare costs. Since hospitals represent the largest share of the healthcare budget, they are required to improve their operational efficiency by making better use of their resources. In particular, hospitals that provide patients direct care and access to expensive resources such as surgeons, operating rooms, ICUs and wards are under scrutiny on whether or not they manage their resources effectively. Operations research scholars have extensively addressed problems related to resource planning and scheduling in hospitals for many years. Conventional analytical models aim to improve the efficiency of decision-making in hospital resource planning at strategic (long-term), tactical (mid-term) and operational (short-term) levels. However, these models suffer from limited ability in effectively capturing the inherent complexity, variability and uncertainty of hospital operations because they often assume crude and simplistic statistical distributions to imitate these operations. Consequently, they are suboptimal in real-life settings. With the massive amount of data gathered in the hospitals and healthcare systems and advances in the field of predictive modeling, the operations research community are now given the opportunity to better analyze, understand and replicate the complexity, variability and uncertainty of hospital operations. To this end, the main objective of this thesis is to develop integrate predictive frameworks that are capable of analyzing and extracting important patterns from large-scale data that better inform hospital resource planning and scheduling systems at the strategic, tactical and operational levels. To the best of the author’s knowledge, this thesis is a pioneer in proposing frameworks for the design of hospital-wide integrative predictive support systems. At the strategic and tactical level, the first article (Chapter 4) develops a hybrid machine learning-simulation framework for predicting personalized patient demand for hospital resources. The framework captures the long-term relationship between hospitals and chronic patients, which spans over a long-term horizon and incorporates the fact that patients will need, not one, but several visits to the hospital and access to various types of resources over a long time period. In this thesis, we propose a novel approach based on deep feedforward neural network model that models the complex interactions of chronic patients with hospital resources during their treatment pathways. The proposed novel approach does so by developing a series of sequential individually trained deep feedforward neural networks, where each network’s input is set as the prediction output of its preceding. The proposed models are capable of predicting patient’s next treatment with an accuracy (measured by “recall”) ranging from 68% to 79%. In addition to predicting the transition of patients between treatments in their clinical pathways, we propose a second series of temporal deep feedforward neural network models that provide the expected receiving time for the next treatment. The trained pathway and temporal predictive models are incorporated into an agent-based simulation which is capable of predicting personalized and aggregated demand for hospitals’ scarce resources for the mid-term and long-term time horizon. We applied the proposed integrative framework to real hospital data and showed that proposed framework effectively predicts mid-term and long-term demand for hospital scarce resources with an accuracy of 77% and 64%, respectively, which dramatically outperforms traditional demand forecasting methods and standard machine learning techniques. At the tactical and operational level, the article proposed in chapter 5 is an integrative predictive model for personalized surgical procedure durations. The framework is the first of its kind to incorporate scheduling-related, operational and temporal attributes in addition to patient specific, procedure specific and surgeon specific attributes to predict surgical procedure durations. Furthermore, the framework illustrates the effectiveness of machine learning algorithms such as Random Forest and Support Vector Machine to capture the complex relationships among the predictors of surgical procedure durations. We applied the proposed framework to real hospital data and found an improvement of 31% in the accuracy of our predictive model compared to its practice benchmark. Furthermore, the results show that scheduling-related decisions such as procedure sequencing and block assignment have a significant impact on surgical procedure durations. This result has significant implications for operating room planning and scheduling literature at both tactical and operational levels. Namely, it indicates that optimal operating room planning is achieved only through joint optimization of surgical duration procedures and schedules. At the operational level, the article presented in chapter 6 proposes an integrative predictive model for operational failure risk assessment. Interestingly, even the most accurate predictive tools used in practice fall short in predicting variability in hospital processes with 100% accuracy. Therefore, at the operational level it is important to avoid decisions that have a high risk of failure which may subsequently result in significant adverse consequences, which, in turn, may incur additional costs, lower quality of care and cause patient and staff dissatisfaction. In this thesis, we apply probabilistic machine learning techniques to the operating room overtime problem. We show that the proposed algorithms are capable of classifying operating room schedules that result in overtime with an accuracy of 88% when applied to real hospital data. The predictive performance is further improved through the use of calibration techniques applied to the output of machine learning algorithms. The proposed risk model has significant implications for practice and operational level resource scheduling literature. First, the proposed risk model can easily be integrated into operating room scheduling systems at the hospital which ultimately assist decision makers in avoiding risky schedules. Second the proposed risk model may be used in conjunction with existing operating room scheduling models to improve the operational performance of commonplace solutions