A review of the healthcare-management (modeling) literature published at Manufacturing and Service Operations Management

Healthcare systems throughout the world are under pressure to widen access, improve efficiency and quality of care, and reduce inequity. Achieving these conflicting goals requires innovative approaches, utilizing new technologies, data analytics, and process improvements. The operations management community has taken on this challenge: more than 10% of articles published in M&SOM in the period from 2009 to 2018 has developed analytical models that aim to inform healthcare operational decisions and improve medical decision-making. This article presents a review of the research published in M&SOM on healthcare management since its inception 20 years ago and reflects on opportunities for further research

Understanding information exchange in healthcare operations:evidence from hospitals and patients

Coordination–or the information exchange among physicians and hospital staff–is necessary for desirable patient outcomes in healthcare delivery. However, coordination is difficult because healthcare delivery processes are information intensive, complex and require interactions of hospitals with autonomous physicians working in multiple operational systems (i.e. multiple hospitals). We examine how three important variables distinctive of the healthcare operations context–use of IT for dissemination of test results (ITDR) (i.e. Electronic Health Records systems) by physicians and hospital staff, social interaction ties among them, and physician employment–influence information exchange and patient perceptions of their care. Drawing from the literature on process inter-dependencies and coordination, vertical integration and social exchange, we develop and test research hypotheses linking ITDR, social interaction ties and physician employment to information exchange relationship, and information exchange relationship to provider-patient communication. Using a paired sample of primary survey data and secondary archival data from CMS HCAHPS for 173 hospitals in the U.S.A., we find that increased information exchange relationship drives provider-patient communication, and increased social interaction ties drives information exchange relationship. Social interaction ties fully mediates the relationship between ITDR and information exchange relationship. Physician employment amplifies the link between ITDR and social interaction ties, but does not have an effect on the link between ITDR and information exchange. We do not find a direct relationship between ITDR, and information exchange relationship or provider-patient communication

Linking electronic medical records use to physicians’ performance:a contextual analysis

Electronic Medical Records (EMR) studies have broadly tested EMR use and outcomes, producing mixed and inconclusive results. This study carefully considers the healthcare delivery context and examines relevant mediating variables. We consider key characteristics of: 1) interdependence in healthcare delivery processes, 2) physician autonomy, and 3) the trend of hospital employment of physicians, and draw on theoretical perspectives in coordination, shared values, and agency to explain how the use of EMR can improve physicians’ performance. In order to examine the effects of physician employment on work practices in the hospital, we collected 583 data points from 302 hospitals in 47 states in the USA to test two models; one for employed and another for non-employed physicians. Results show that information sharing and shared values among healthcare delivery professionals fully mediate the relationship between EMR use and physicians’ performance. Next, physician employment determines which mediating variable constitutes the pathway from EMR use to physicians’ performance. Finally, we highlight the impact of shared values between the hospital and physicians in enhancing information sharing and physicians’ performance, extending studies of these behaviors among network partners in industrial settings. Overall our study shows that EMR use should be complemented by processual (information sharing), social (shared values) and structural (physician employment) mechanisms to yield positive effects on physicians’ performance

Essays in Operations Management: Applications in Health Care and the Operations-Finance Interface

I present three essays pertaining to the management of supply chain risks in this dissertation. The first essay and the second essay analyze supply chain risks from a financial perspective, while the third essay analyzes supply chain risk with the objective of maximizing societal benefits in health care. In my first essay, I consider a firm facing inventory decisions under the influence of the financial market. With stochastic analytical methods, the purpose of this essay is to examine the optimal inventory decisions under a variety of conditions. I have identified the relevant factors impacting such decisions and the firm's value. Moreover, I have studied the benefits brought by efforts to improve the random capacity of the firm. I conclude that the financial market can significantly impact both a firm's inventory decisions and process improvement incentives. In my second essay, I model a stylized supply chain managed by a base-stock inventory policy where the decision maker holds concerns about the down-side risk of the supply chain cost. With stochastic analytical methods, the purpose of this essay is to obtain solutions of the problem of minimizing Conditional Value-at-Risk under various supply chain scenarios. I find that various supply chain parameters may influence the optimal solution and the optimality of a stock-less operation. I conclude that operating characteristics of a supply chain can shape its inventory policy when down-side risks are taken into account. For my third essay, the purpose of this essay is to investigate the operational decisions of a medical center specializing in bone marrow transplants. Using the queuing system method, I formulate the medical center as a queuing system with random patient arrivals and departures. I find optimal decisions and efficient frontiers regarding waiting room size and the number of transplant rooms with the objective of maximizing patient health benefits. I conclude that the design of a health care delivery system is crucial for health care institutions to sustain and improve their social impacts. In each of the three essays, I use analytical and numerical approaches to optimize managers' decisions with respect to various sources of risk

Factors Affecting the Financial Performance of US Children’s Hospitals: An Exploratory Study

Financial performance is a key indicator of success and competitive advantage for organizations. This paper presents an exploratory study of factors affecting financial performance of US children’s hospitals using secondary data collected by the American Hospital Association. The dataset included all children’s hospitals in the United States. Prior studies explored factors around financial performance of hospitals in general, but to date, there is no enough literature that focuses on children’s hospitals to explore which factors impact financial performance independently and simultaneously. While many factors may affect financial performance, but this study found that: health care accessibility, number of services offered, organizational factors and community factors to be the most significant predictors of financial performance independently and simultaneously. This exploratory study used an empirical quantitative method to examine the characteristics of these independent variables using the resource-based view(RBV) as a theoretical framework. The study offered practical solutions for hospital managers and practitioners. It made valuable recommendation for future research and new addition to the body of knowledge and the literature in this domain of study. Hospital leaders can use these empirical findings to develop financial strategies to increase children’s hospitals overall revenue

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

Operating on Quality, Access, and Cost: Managing Better Health Systems

Current spending in the US health system has reached 17.9% of GDP and the need for improved decision-making to help lower costs and improve quality is widely recognized. This dissertation, Operating on Quality, Access, and Cost: Managing Better Health Systems takes a hierarchical approach to effective healthcare decision-making by examining three broad areas for healthcare improvement: health systems design, health systems maintenance, and clinical operations. We examine how effective operational strategies for improving health service delivery take into account interrelationships between quality, access, and cost of care. At the design level, we employ competitive queueing models to study the impact of inter-provider competition on quality, wait-time and social welfare. At the maintenance level, we use queueing network analysis to study the relationship between screening guidelines and capacity planning for colorectal cancer. At the operations level, we employ stochastic modeling to analyze appointment allocation policies to improve outpatient clinics' responsiveness to patients' needs.Doctor of Philosoph