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Problem definition: Delay announcements have become an essential tool in service system operations: They influence customer behavior and network efficiency. Most current delay announcement methods are designed for relatively simple environments with a single service station or stations in tandem. However, complex service systems, such as healthcare systems, often have fork-join (FJ) structures. Such systems usually suffer from long delays as a result of both resource scarcity and process synchronization, even when queues are fairly short. These systems may thus require more accurate delay estimation techniques than currently available. Methodology/results: We analyze a network comprising a single-server queue followed by a two-station FJ structure using a recursive construction of the Laplace–Stieltjes transform of the joint delay distribution, conditioning on customers’ movements in the network. Delay estimations are made at the time of arrival to the first station. Using data from an emergency department, we examine the accuracy and the robustness of the proposed approach, explore different model structures, and draw insights regarding the conditions under which the FJ structure should be explicitly modeled. We provide evidence that the proposed methodology is better than other commonly used queueing theory estimators such as last-to-enter-service (which is based on snapshot-principle arguments) and queue length, and we replicate previous results showing that the most accurate estimations are obtained when using our model result as a feature in state-of-the-art machine learning estimation methods. Managerial implications: Our results allow management to implement individual, real-time, state-dependent delay announcements in complex FJ networks. We also provide rules of thumb with which one could decide whether to use a model with an explicit FJ structure or to reduce it to a simpler model requiring less computational effort. Funding: This work was supported by the Dutch Research Council (NWO) Gravitation Programme NETWORKS [Grant 024.002.003], the Israel Ministry of Science and Technology [Grant 880011], and the Israel Science Foundation [Grant 1955/15]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.1167 .

In order to perform a surgical procedure, substantial numbers of sterile instruments should be readily available to surgeons through the containers referred to as surgical trays and peel packs. After the procedure, all instruments in the opened containers, regardless of whether they have been used or not, must go through the labor-intensive re-sterilization process. Empirical studies have shown that the utilization rate of instruments within trays is very low due to not having optimized tray configurations. Additionally, surgical trays often include instruments that are not likely to be used but are included “just in case”, which imposes an additional cost on hospitals through unnecessary instrument re-sterilization. This study is the first analytical attempt to address the issue of configuring surgical trays based on the likelihood of instrument usage through formulating and solving a probabilistic tray optimization problem (PTOP). The PTOP model can serve as a decision support for surgeons by providing them with the tray’s probability of being used for optimally configured trays and its associated reprocessing costs. The PTOP is constructed upon an integer non-linear programming model. A decomposition-based heuristic and metaheuristic method coupled with two novel local search algorithms are developed to solve the PTOP. The application of this model can be illustrated through a case study. We discuss how hospitals could benefit from our model in reducing the costs associated with opening trays unnecessarily before a procedure. Additionally, we conducted a risk analysis to estimate the level of confidence for the recommended solution.

This paper investigates the role of resource allocation in alleviating the impact on from disruptions in healthcare operations. We draw on resource orchestration theory and analyse data stemming from US healthcare to discuss how the US healthcare system structured, bundled and reconfigured resources (i.e. number of hospital beds, and vaccines) during the COVID-19 pandemic. Following a comprehensive and robust econometric analysis of two key resources (i.e. hospital beds and vaccines), we discuss its effect on the outcomes of the pandemic measured in terms of confirmed cases and deaths, and draw insights on how the learning curve effect and other factors might influence in the efficient and effective control of the pandemic outcomes through the resource usage. Our contribution lies in revealing how different resources are orchestrated (‘structured’, ‘bundled’, and ‘leveraged’) to help planning responses to and dealing with the disruptions to create resilient humanitarian operations. Managerial implications, limitations and future research directions are also discussed.

Maitha Alkaabi,1 Mecit Can Emre Simsekler,1 Raja Jayaraman,1 Abdulqader Al Kaf,1 Hussam Ghalib,2 Dima Quraini,2 Samer Ellahham,2 E Murat Tuzcu,2 Kudret Demirli3 1Department of Industrial and Systems Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates; 2Heart and Vascular Institute, Cleveland Clinic Abu Dhabi, Abu Dhabi, United Arab Emirates; 3Department of Mechanical, Industrial and Aerospace Engineering, Concordia University, Montreal, CanadaCorrespondence: Mecit Can Emre SimseklerDepartment of Industrial and Systems Engineering, Khalifa University of Science and Technology, P.O. Box 127788, Abu Dhabi, United Arab EmiratesTel +9712 501 8410Fax +971 2 447 2442Email emre.simsekler@ku.ac.aePurpose: Waste identification plays a vital role in lean healthcare applications. While the value stream map (VSM) is among the most commonly used tools for waste identification, it may be limited to visualize the behaviour of dynamic and complex healthcare systems. To address this limitation, system modelling techniques (SMTs) can be used to provide a comprehensive picture of various system-wide wastes. However, there is a lack of evidence in the current literature about the potential contribution of SMTs for waste identification in healthcare processes.Methods: This study evaluates the usability and utility of six types of SMTs along with the VSM. For the evaluation, interview-based questionnaires were conducted with twelve stakeholders from the outpatient clinic at the Heart and Vascular Institute at Cleveland Clinic Abu Dhabi.Results: VSM was found to be the most useful diagram in waste identification in general. However, some SMTs that represent the system behaviour outperformed the VSM in identifying particular waste types, e.g., communication diagram in identifying over-processing waste and flow diagram in identifying transportation waste.Conclusion: As behavioural SMTs and VSM have unique strengths in identifying particular waste types, the use of multiple diagrams is recommended for a comprehensive waste identification in lean. However, limited resources and time, as well as limited experience of stakeholders with SMTs, may still present obstacles for their potential contribution in lean healthcare applications.Keywords: lean healthcare, value stream map, system modelling techniques, waste identification, kaizen, system approaches, healthcare operations

Rising healthcare costs force healthcare organizations worldwide to reconsider their business strategies. Hospital managers are tasked with lowering operating costs while upholding the high quality of care, two goals perceived as contradictory by many healthcare professionals. Attempting to contribute towards reaching these goals, the study explores the digitalization of the healthcare supply chain through the adoption of enterprise resource planning (ERP) systems and its impact on hospital supply chain costs. Established technology adoption theories are synthesized to develop a research model that investigates the adoption of hospital ERP systems and its impact on supply chain cost performance. The framework is validated via structural equation modeling (SEM) with data collected from 107 Greek public hospitals. The results indicate that technological and organizational readiness, hospital size, governmental policies, and perceived benefits have a significant influence on the extent of ERP systems adoption. They also demonstrate a statistically significant association between the use of ERP systems and hospital supply chain costs. The present study highlights the influencing factors of ERP systems adoption in hospitals and provides empirical evidence on the resulting cost benefits., This implies greater urgency for hospitals to digitalize their supply chain by implementing and fully exploiting the ERP systems’ functionality in order to reach their goal of delivering quality care at a lower cost.

The tremendous advances in machine learning and optimization over the past decade have immensely increased the opportunity to personalize and improve decisions for a plethora of problems in healthcare. This brings forward several challenges and opportunities that have been the primary motivation behind this dissertation and its contributions in both practical and theoretical aspects. This dissertation is broadly about sequential decision-making and statistical learning under limited resources. In this area, we treat sequentially arriving individuals, each of which should be assigned to the most appropriate resource. Per each arrival, the decision-maker receives some contextual information, chooses an action, and gains noisy feedback corresponding to the action. The aim is to minimize the regret of choosing sub-optimal actions over a time horizon. We provide data-driven and personalized methodologies for this class of problems. Our data-driven methods adaptively learn from data over time to make efficient and effective real-time decisions for each individual, when resources are limited. With a particular focus on high-impact problems in healthcare, we develop new online algorithms to solve healthcare operations problems. The theoretical contributions lie in the design and analysis of a new class of online learning algorithms for sequential decision-making and proving theoretical performance guarantees for them. The practical contributions are to apply our methodology to solve and provide managerial and practical insights for problems in healthcare, service operations, and operations management in general.

This thesis intersects traditional operations management and data analytics with clinical applications to provide insight into the patient journey ��� the process of how patient seek and receive care from the healthcare system. In the first section of the thesis, we empirically analyze the beginning stage of the patient journey of opioid use. In the United States alone, the opioid epidemic contributes to tens of thousands of deaths each year, and the government has funded billions of dollars of interventions including prescription drug monitoring programs and tamper-resistant opioid formulations. As most research is focused on chronic opioid users, little is known about how operational interventions shortly after opioid initiation can impact a patient���s likelihood of long-term opioid use. We combine medical and pharmaceutical claims to investigate the care delivery process for patients who initiate opioid prescriptions in the primary care setting. Of the patients who return to the primary care setting for a follow-up appointment, we estimate the impact of provider discordance (i.e. seeing a clinician other than the original opioid prescriber for the follow-up appointment) on long-term opioid use. A series of controlled logistic regressions, instrumental variable analyses, and propensity score matching (with a bias minimization method) establish a significant causal effect in the presence of unobserved confounders. Additionally, we analyze a potential mechanism underlying the main effect and investigate the factors that could help inform a potential process intervention. The analyses provide robust evidence that provider discordance during opioid initiation could be a promising and hitherto untapped opportunity to reduce the influx of patients afflicted by the opioid epidemic. In the second section of this thesis, we take a broader view of the patient journey and develop a methodology to extract a clinically meaningful representation of patient journey diagnosis and treatment patterns from claims data. While medical and pharmaceutical claims are a rich source of data in healthcare research, they are also notoriously difficult to analyze. We first process claims into strings of characters that represent the series of events (e.g., distinct interactions with the healthcare system) in the patient journey. Then we adapt the dynamic program underlying the Levenshtein edit distance sequence alignment algorithm to evaluate the level of similarity between each pair of episodes. The similarity scores serve as inputs to an unsupervised clustering algorithm that extracts the main patterns of the patient journey. When the methodology is applied to the context of mechanical back and neck pain, it yields an overview of the main treatment patterns that had not been previously captured in the medical literature. The methodology also enables investigation into self-triage: how the patient���s first decision of where to seek care is associated with longer-term variation in treatment. We identify clear differences in patient-level socio-economic and clinical characteristics between both the choice of initial site of care and treatment patterns, highlighting the potential public health impact of this methodology. Overall, we show that applying data mining and econometrics to claims data yields insights that contribute to both the medical and operations fields., Cambridge International Trust Scholarship, Cambridge Judge Business School, Cambridge School of Technology Fieldwork Fund

With population growth and aging, the emergence of new diseases and immunodeficiency, the demand for emergency departments (EDs) increases, making overcrowding in these departments a global problem. Due to the disease severity and transmission rate of COVID-19, it is necessary to provide an accurate and automated triage system to classify and isolate the suspected cases. Different triage methods for COVID-19 patients have been proposed as disease symptoms vary by country. Still, several problems with triage systems remain unresolved, most notably overcrowding in EDs, lengthy waiting times and difficulty adjusting static triage systems when the nature and symptoms of a disease changes. In this paper, we conduct a comprehensive review of general ED triage systems as well as COVID-19 triage systems. We identified important parameters that we recommend considering when designing an e-Triage (electronic triage) system for EDs, namely waiting time, simplicity, reliability, validity, scalability, and adaptability. Moreover, the study proposes a scoring-based e-Triage system for COVID-19 along with several recommended solutions to enhance the overall outcome of e-Triage systems during the outbreak. The recommended solutions aim to reduce overcrowding and overheads in EDs by remotely assessing patients’ conditions and identifying their severity levels.

Healthcare facilities require flexible layouts that can adapt quickly in the face of various disruptions. COVID-19 confirmed this need for both healthcare and manufacturing systems. Starting with the transfer of decision support systems from manufacturing, this paper generalizes layout re-design activities for complex systems by presenting a simulation framework. Through a real case study concerning the proliferation of nosocomial cross-infection in an intensive care unit (ICU), the model developed in systems dynamics, based on a zero order immediate logic, allows reproducing the evolution of the different agencies (e.g., physicians, nurses, ancillary workers, patients), as well as of the cyber-technical side of the ICU, in its general but also local aspects. The entire global workflow is theoretically founded on lean principles, with the goal of balancing the need for minimal patient throughput time and maximum efficiency by optimizing the resources used during the process. The proposed framework might be transferred to other wards with minimal adjustments; hence, it has the potential to represent the initial step for a modular depiction of an entire healthcare facility.

Improving the delivery of patient care is an ongoing challenge in the National Health Service (NHS). This challenge is not insignificant in the process of chemotherapy administration for oncology patients. The present research is motivated by a public Spanish hospital in which oncology patients receive medical care in the Oncology Day Hospital (ODH). At the ODH, oncology patients receive different health services by different specialists on a single day. Any discoordination in patient flow will contribute to longer waiting times and stays in the ODH. As oncology patients tend to have special health conditions, any extra time in the hospital is a source of risk and discomfort. This study applies value stream mapping methodology in a Spanish ODH to improve this situation, reducing hospital waiting times and shorting the length of stay. For that purpose, the path of the oncology patients is mapped and the current state of the system is analyzed. Working at takt time and levelling the workload are proposed for improving the working conditions for healthcare personnel. As a result, the quality of service for oncology patients who need a well-defined care profile is improved. The singular characteristics of the Spanish NHS make it challenging to implement new ways of working, so this study has significant theoretical and managerial implications offering directions in which improvement is possible.

Maitha Alkaabi,1 Mecit Can Emre Simsekler,1 Raja Jayaraman,1 Abdulqader Al Kaf,1 Hussam Ghalib,2 Dima Quraini,2 Samer Ellahham,2 E Murat Tuzcu,2 Kudret Demirli3 1Department of Industrial and Systems Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates; 2Heart and Vascular Institute, Cleveland Clinic Abu Dhabi, Abu Dhabi, United Arab Emirates; 3Department of Mechanical, Industrial and Aerospace Engineering, Concordia University, Montreal, CanadaCorrespondence: Mecit Can Emre SimseklerDepartment of Industrial and Systems Engineering, Khalifa University of Science and Technology, P.O. Box 127788, Abu Dhabi, United Arab EmiratesTel +9712 501 8410Fax +971 2 447 2442Email emre.simsekler@ku.ac.aePurpose: Waste identification plays a vital role in lean healthcare applications. While the value stream map (VSM) is among the most commonly used tools for waste identification, it may be limited to visualize the behaviour of dynamic and complex healthcare systems. To address this limitation, system modelling techniques (SMTs) can be used to provide a comprehensive picture of various system-wide wastes. However, there is a lack of evidence in the current literature about the potential contribution of SMTs for waste identification in healthcare processes.Methods: This study evaluates the usability and utility of six types of SMTs along with the VSM. For the evaluation, interview-based questionnaires were conducted with twelve stakeholders from the outpatient clinic at the Heart and Vascular Institute at Cleveland Clinic Abu Dhabi.Results: VSM was found to be the most useful diagram in waste identification in general. However, some SMTs that represent the system behaviour outperformed the VSM in identifying particular waste types, e.g., communication diagram in identifying over-processing waste and flow diagram in identifying transportation waste.Conclusion: As behavioural SMTs and VSM have unique strengths in identifying particular waste types, the use of multiple diagrams is recommended for a comprehensive waste identification in lean. However, limited resources and time, as well as limited experience of stakeholders with SMTs, may still present obstacles for their potential contribution in lean healthcare applications.Keywords: lean healthcare, value stream map, system modelling techniques, waste identification, kaizen, system approaches, healthcare operations

The main objective when managing inventories in blood supply chains is to establish an efficient balance between the wastage and shortage of blood units. The uncertain demand and the perishable nature of blood units can result in over- or under-stocking and increase wastage and shortage costs. In this study, we analyze how a proactive transshipment policy can avoid future shortages in addition to mitigate wastage. We consider a network of hospitals with uncertain demand in which each hospital makes decisions on the quantity to order from a central blood bank and to transship to other hospitals in each review period. We formulate the problem as a two-stage stochastic programming model. To generate scenarios, the Quasi-Monte Carlo sampling approach is employed and the optimal number of scenarios is determined by conducting stability tests. We performed numerical experiments to evaluate the performance of the proposed model and investigate the potential benefits of the outlined proactive transshipment. The developed model is used to compare the optimized policy with the current practice in some hospitals in Australia and with a no-transshipment policy. The numerical results indicate significant potential cost savings in comparison with the current policy in use and the no-transshipment policy.

Emergency departments (EDs) are continuously exploring opportunities to improve their efficiency. A new opportunity lies in revising the patient-physician assignment process by limiting the number of patients simultaneously assigned to a single physician, which is defined as the application of a case manager approach with limited caseloads. The potential of introducing a case manager approach with limited caseloads as a way to improve physician productivity, and consequently ED performance, is investigated by use of a discrete-event simulation model based on a real-life case study. In addition, as the case manager system is characterised by three parameters that can be customised and optimised (i.e. caseload limit, pre-assignment queueing discipline and internal queueing discipline), the impact of these parameters on the effectiveness to improve ED performance in terms of length-of-stay and door-to-doctor time is evaluated. To the best of our knowledge, this paper is the first to examine the potential of a case manager system with limited caseloads in a complex service system like a real-life ED, and to investigate the impact of the three system parameters on the results. The outcomes of the study show that performance can be improved significantly by introducing a case manager system, and that the system parameters have an impact on the effect size. This work is supported by the Strategic Basic Research project Data-driven logistics (S007318N), funded by the Research Foundation Flanders (FWO). This work is supported by the Special Research Fund (BOF) of Hasselt University (BOF20TT03).

Belirsizlik varken ayaktan kemoterapi randevularının çizelgelenmesi esnasında hemşirelerin iş yükünü dengelemek zor bir problemdir. Bu çalışmada, hastaların randevu vakitlerinin belirlenmesini ve hemşirelerin kendi aralarındaki iş yükü dengesini bozmadan hastaların koltuklara ve hemşirelere atanmasını sağlayan iki aşamalı stokastik karışık tamsayılı programlama modeli tasarlanmıştır. Çalışmada pre-medikasyon ve infüzyon sürelerindeki belirsizlik dikkate alınmıştır. Modelin amaç fonksiyonu, hastaların bekleme süreleri ve hemşirelerin fazla mesai sürelerinin ağırlıklandırılmış toplamının beklenen değerini enküçüklemektedir. Büyük bir hastanenin ayaktan kemoterapi ünitesinden elde edilmiş veriler kullanılarak bilgisayısal deneyler yapılmıştır. Çalışmada gözetilen iki rakip ölçüt arasındaki denge incelenmiştir. Klinikte bulunan koltuk ve hemşire sayısı ile performans ölçütleri arasındaki ilişki irdelenmiştir. Stokastik çözüm değeri hesaplanarak, belirsizliği dikkate alarak çözüm bulmanın faydası ölçülmüştür., Balancing nurse workload while scheduling outpatient chemotherapy appointments under uncertainty is a challenging problem. In this study, a two-stage stochastic mixed-integer programming model is proposed for setting appointment times of patients and assigning patients to chairs and nurses without distorting the workload balance among nurses. The uncertainty in pre-medication and infusion durations is considered. The objective function of the model minimizes the expected weighted sum of patient waiting time and nurse overtime. Computational experiments are conducted based on data from an outpatient chemotherapy unit of a large hospital. The trade-off between the two competing criteria of the study is investigated. The relationship between the numbers of chairs and nurses in the clinic with the performance measures is examined. The benefit of considering uncertainty is assessed by calculating the value of stochastic solution.

Functional Resonance Analysis Method (FRAM) is a function-based approach to model complex socio-technical systems and to manage variability. The current FRAM related tools are unable to capture qualitative and quantitative characteristics of variability as well as temporal variations. This study presents in detail a dynamic FRAM-based tool, which is called DynaFRAM. It is introduced to address the variability-related deficiencies of the FRAM related tools. It aims to capture variability in complex operations. It is a dynamic tool developed to capture time related variations in complex operations. This increases the attractiveness of the DynaFRAM for complex operations where specialists and practitioners make decisions in complicated situations. The ability of the DynaFRAM is demonstrated by examining a healthcare related case study. Although the ability of the DynaFRAM is assessed through capturing variations in healthcare operations, it can be applied to other domains in a similar manner.•The DynaFRAM is a dynamic FRAM-based tool.•It is able to captures different characteristics of variability.•It facilitates understanding and analysis of variability in complex operations., Graphical abstract Image, graphical abstract

Background Patient satisfaction is a multi-dimensional concept that provides insights into various quality aspects in healthcare. Although earlier studies identified a range of patient and provider-related determinants, their relative importance to patient satisfaction remains unclear. Methods We used a tree-based machine-learning algorithm, random forests, to estimate relationships between patient and provider-related determinants and satisfaction level in two of the main patient journey stages, registration and consultation, through survey data from 411 patients at a hospital in Abu Dhabi, UAE. Radar charts were also generated to determine which type of questions—demographics, time, behaviour, and procedure—influence patient satisfaction. Results Our results showed that the ‘age’ attribute, a patient-related determinant, is the leading driver of patient satisfaction in both stages. ‘Total time taken for registration’ and ‘attentiveness and knowledge of the doctor/physician while listening to your queries’ are the leading provider-related determinants in each model developed for registration and consultation stages, respectively. The radar charts revealed that ‘demographics’ are the most influential type in the registration stage, whereas ‘behaviour’ is the most influential in the consultation stage. Conclusions Generating valuable results, the random forest model provides significant insights on the relative importance of different determinants to overall patient satisfaction. Healthcare practitioners, managers and researchers can benefit from applying the model for prediction and feature importance analysis in their particular healthcare settings and areas of their concern.

This book introduces blockchain technology applications in supply chains. Blockchain is a relatively new tool, nevertheless, there have been considerable advances over the last five years, and blockchain is now poised to revolutionize the conventional supply chains with the offering of accountability and quality to the wider complex supply networks. Based on literature reviews and original research, this book serves as an essential introduction to blockchain and its applications in supply chain. The unique features of the book are empirical studies to demonstrate the application of blockchain technology in food, healthcare, manufacturing, transportation and retail sectors. Each chapter includes research framework and open research questions. Simple narration of concept and detailed insights from primary research information. Use case narrative will provoke the readers to demystify the myths in application of concepts in the supply chain. Overall, the book demystifies blockchain technology, reviews evolution and outlines its future applications by blending contents to meet the expectations of both academic and practice community.

When urgent situations occur (e.g., inaccurate demand forecast, traffic accidents, or infectious disease outbreaks), the stock of rescue medical items in the hospital might not be enough to cater to the drastically increased demand. Comparing with placing an expensive emergent replenishment order with dealers, requesting inventory sharing from another hospital with excessive stocks could save time and cost. This paper investigates the operation of the inventory sharing mechanism between two independent hospitals with the consideration of patient behavior. We first identified the inventory decisions when hospitals are under a no-sharing scenario, and derive hospitals&rsquo, sharing policies and inventory policies under the sharing scenario. Through numerical experiments, we found that the inventory sharing option is profitable for hospitals compared to emergent replenishment. Furthermore, we investigated the effects of patient behavior, safety inventory level of the hospital, and other cost parameters on inventory decisions. Under the sharing policy, the increase of hospital j&rsquo, s emergent request rate and safety inventory level increased the optimal initial inventory level of hospital i, while the increase of hospital j&rsquo, s initial inventory level decreased the optimal inventory level of hospital i. This paper provides more practical suggestions for hospitals&rsquo, inventory sharing operation.

This study aimed to analyze the process of implementing lean healthcare in three Brazilian hospitals that have played a pioneering role in the implementation of this system. We adopted the case study approach and collected data through interviews, document analysis, and observation. The results showed that the processes of implementation of lean healthcare in the hospitals shared many similarities in terms of the flow of patients, materials, or information. Finally, it was possible to obtain some guidelines for the application of lean principles and techniques in order to facilitate the implementation of lean healthcare in hospital operations.

RESUMO Este artigo tem como principal objetivo analisar como ocorreu o processo de implementação do lean healthcare em três hospitais brasileiros que exerceram um papel pioneiro na aplicação desse sistema. Foi utilizada a abordagem metodológica do estudo de caso, adotando como técnicas de coleta de dados entrevistas, análise documental e observação. Os resultados evidenciaram que os processos de implementação da produção enxuta nos três hospitais tiveram muitas semelhanças, seja nos fluxos de pacientes, materiais ou informações. Por meio da pesquisa, foi possível extrair diretrizes quanto à aplicação das técnicas e dos princípios enxutos de modo a fornecer suporte às operações hospitalares na implementação da produção enxuta. RESUMEN Este artículo tiene como principal objetivo analizar cómo se dio el proceso de implementación del lean healthcare en tres hospitales brasileños que desempeñaron un papel pionero en la aplicación de dicho sistema. Se utilizó el abordaje metodológico del estudio de caso, y se adoptaron como técnicas de recolección de datos entrevistas, análisis documental y observación. Los resultados evidenciaron que los procesos de implementación del lean healthcare en los tres hospitales han tenido muchas semejanzas, ya sea en los flujos de pacientes, material o información. Finalmente, mediante la investigación, fue posible extraer directrices respecto a la aplicación de las técnicas y de los principios lean a los efectos de proporcionar soporte a las operaciones hospitalarias en la implementación del lean healthcare ABSTRACT This study aimed to analyze the process of implementing lean healthcare in three Brazilian hospitals that have played a pioneering role in the implementation of this system. We adopted the case study approach and collected data through interviews, document analysis, and observation. The results showed that the processes of implementation of lean healthcare in the hospitals shared many similarities in terms of the flow of patients, materials, or information. Finally, it was possible to obtain some guidelines for the application of lean principles and techniques in order to facilitate the implementation of lean healthcare in hospital operations.

International audience; Motivated by an increasing adoption of evidence-based medical guidelines in the delivery of medical care, we examine whether increased adherence to such guidelines (typically referred to as higher process quality) is associated with reduced resource usage in the course of patient treatment. In this study, we develop a sample of US hospitals and use cardiac care as our context to empirically examine our questions. To measure a patient's resource usage, we use the total length of stay, which includes any additional inpatient stay necessitated by unplanned readmissions within thirty days after initial hospitalization. We find evidence that higher process quality, and more specifically its clinical (as opposed to its administrative) dimensions, are associated with a reduction in resource usage. Moreover, the standardization of care that is achieved via the implementation of medical guidelines, makes this effect more pronounced in less focused environments: higher process quality is more beneficial when the cardiac department's patient population is distributed across a wider range of medical conditions. We explore the implications of these findings for process-oriented pay-for-performance programs, which tie the reimbursement of hospitals to their adherence to evidence-based medical guidelines.

Due to copyright restrictions, the access to the full text of this article is only available via subscription. This study investigates appointment systems (AS), as combinations of access rules and appointment-scheduling rules, explicitly designed for dealing with walk-in seasonality. In terms of 'access rules', strategies are tested for adjusting capacity through intra-week, or monthly seasonality of walk-ins, or their combined effects. In terms of 'appointment rules', strategies are tested to determine which particular slots to double-book or leave open in cases where seasonal walk-in rates exceed or fall short of the overall yearly rate. In that regard, this study integrates capacity and appointment decisions, which are usually addressed in an isolated manner in previous studies. Simulation optimization is used to derive heuristic solutions to the appointment-scheduling problem, and the findings are compared in terms of in-clinic measures of patient wait time, physician idle time and overtime. The goal is to provide practical guidelines for healthcare practitioners on how to best design their AS when seasonal walk-ins exist. TÜBİTAK

ObjectivesTo introduce serialised medicines into an operational hospital dispensary and assess the technical effectiveness of digital medicine authentication (MA) technology under European Union Falsified Medicines Directive (EU FMD) conditions.DesignThirty medicine lines were serialised using 2D data matrix labels and introduced into an operational UK National Health Service (NHS) hospital dispensary. Staff were asked to check medicines for two-dimensional (2D) data matrices and scan those products, in addition to their usual medicine preparation and checking processes. Four per cent of the study medicines were labelled with a 2D barcode which generated a pop-up, identifying the medicine as either authenticated elsewhere (falsified), authenticated here, expired or recalled.SettingAn NHS teaching hospital based in the UK, the same site as the Naughtonet al2016 study.ParticipantsGeneral Pharmaceutical Council registered, accredited accuracy checking technicians and pharmacists.Primary outcome measuresAverage response times, offline issues, instances of incorrect quarantine and workarounds. The EU FMD maximum response time is 300 milliseconds (ms).ResultsDuring the checking stage of medicine preparation, the average response time for MA in this study was 131 ms. However, 4.67% of attempted authentications experienced offline issues, an increase of 4.23% from the previous study. An increase in offline instances existed alongside an increase in incorrect quarantine.ConclusionsDigital drug screening has the capability of operating with average response times which are below the maximum EU FMD limit of 300 ms. However, there was an increased incidence of offline errors and cases of incorrect quarantine. The practical and legal implications of supplying a substandard or falsified medicine during offline periods without prior authentication or withholding supply until online status resumes are not yet fully understood.

International audience; Motivated by a recent legislative discussion in the European Union about providing European patients with the freedom to choose the country they receive treatment, we use a queueing framework to analyze a game-theoretic model that captures the interactions among the patients, the providers, and the healthcare funders. We examine the impact of such "free choice" on the healthcare systems of different countries in equilibrium. Under the assumption that each patient will always prefer to receive care at home when the waiting time is below her individual tolerance level, we show that, in the long run, cross-border patient movement can increase patient welfare due to increased access to care. However, it has a mixed effect on waiting times and reimbursement rates. Moreover, the additional costs of increased access to care are disproportionately shared between the participating countries.

In this dissertation I look at two problems of resource planning at two major healthcare organizations. The Greater Los Angeles Station of the Veterans Health Administration and the UCLA Ronald Reagan Medical Center.The first chapter of this thesis is a brief introduction to the research presented in subsequent chapters. The second chapter of this thesis considers the problem of minimizing daily expected resource usage and overtime costs across multiple parallel resources such as anesthesiologists and operating rooms, which are used to conduct a variety of surgical procedures at large multi-specialty hospitals. To address this problem, a two-stage mixed-integer stochastic dynamic programming model with recourse is developed. The first stage allocates these resources across multiple surgeries with uncertain durations and prescribes the sequence of surgeries to these resources. The second stage determines actual start times to surgeries based on realized durations of preceding surgeries and assigns overtime to resources to ensure all surgeries are completed using the allocation and sequence determined in the first stage. A data driven robust optimization method that solves large-scale real-sized versions of this model close to optimality is developed. This model is validated and implemented as a decision support system at the UCLA Ronald Reagan Medical Center. This has led to an average daily cost savings of around 7\% or estimated to be \$2.2 million on an annual basis. In addition, the insights based on this model have significantly influenced decision making at the operating services department at this hospital.In the third chapter of this thesis the planning problem for HIV screening, testing, and care is analyzed. This problem consists of determining the optimal fraction of patients to be screened in every period as well as the optimum staffing level at each part of the healthcare system to maximize the total health benefits to the patients measured by quality-adjusted life-years (QALYs) gained. This problem is modeled as a nonlinear mixed integer programming program comprising disease progression (the transition of the patients across health states), system dynamics (the flow of patients in different health states across various parts of the healthcare delivery system), and budgetary and capacity constraints. On applying the model to the Greater Los Angeles (GLA) station in the Veterans Health Administration system, it was found that a Centers for Disease Control and Prevention recommended routine screening policy in which all patients visiting the system are screened for HIV irrespective of risk factors may not be feasible because of budgetary constraints. Consequently, the model was used to develop and evaluate managerially relevant policies within existent capacity and budgetary constraints to improve upon the current risk based screening policy of screening only high risk patients. Our computational analysis showed that the GLA station can achieve substantial increase (20% to 300%) in the QALYs gained by using these policies over risk based screening. The fourth chapter of this thesis concludes with some remarks on future research.

Purpose The purpose of this paper is to present a new outsourcing model for materials management related to the operating theatre of hospitals. Two distinguishing features characterize the model: the long-term collaborative network established among the supplying companies (the “Network factor”), and the implementation of the RFID technology along the supply chain (the “RFID factor”). The network factor allows sharing transportation costs, while the RFID factor allows implementing a continuous review policy, instead of the periodic review policy normally utilized in hospitals. In the paper the effect of these two factors on the minimization of total materials management costs is investigated. Design/methodology/approach An analytical model, validated through a simulation study, is proposed to calculate total management costs of materials, depending on the presence of the network and the RFID factors. Throughout the model it is possible to perform a scenario analysis and individuate the inventory management policy that allows minimizing total costs. The procedure has been applied to a real case study of a long-term collaborative network of supplying companies in the healthcare sector that operates in Central Italy. Findings The optimal inventory management policy strongly depends on the mutual distances of supplying companies and the hospital. Both of the two factors have an impact on the reduction of total annual costs. The analysis of the scenario shows that a positive interaction effect exists between the two factors, so that higher savings are obtained when both factors are present. Originality/value The outsourcing model presented in the paper is new, and the managerial insights that can be drawn from the application of the model to the healthcare sector can be extended to many other industries.

There is not yet any substitute for human blood which remains a scarce resource in many countries. Effective and efficient management of blood supply chains (BSCs) is utmost important in the healthcare industry. This paper gives an overview of the BSC and how blood products are used at hospitals to provide life-saving services to patients. Factoring in the blood types and their receipt compatibility, a simple inventory model is proposed. Using secondary data, the model is illustrated by way of a small case study in Nova Scotia, Canada. We highlight that due to both demand and supply uncertainties, and due to its perishable nature, inventorying blood products is not straightforward and brings with it many logistical and management challenges in the BSC.

We analyzed the planning problem for HIV screening, testing, and care. This problem consists of determining the optimal fraction of patients to be screened in every period as well as the optimum staffing level at each part of the healthcare system to maximize the total health benefits to the patients measured by quality-adjusted life-years (QALYs) gained. We modeled this problem as a nonlinear mixed integer programming program comprising disease progression (the transition of the patients across health states), system dynamics (the flow of patients in different health states across various parts of the healthcare delivery system), and budgetary and capacity constraints. We applied the model to the Greater Los Angeles (GLA) station in the Veterans Health Administration system. We found that a Centers for Disease Control and Prevention recommended routine screening policy in which all patients visiting the system are screened for HIV irrespective of risk factors may not be feasible because of budgetary constraints. Consequently, we used the model to develop and evaluate managerially relevant policies within existent capacity and budgetary constraints to improve upon the current risk based screening policy of screening only high risk patients. Our computational analysis showed that the GLA station can achieve substantial increase (20% to 300%) in the QALYs gained by using these policies over risk based screening. The GLA station has already adapted two of these policies that could yield better patient health outcomes over the next few years. In addition, our model insights have influenced the decision making process at this station.

Helena Carvalho was supported by a PhD fellowship from Fundação para a Ciência e Tecnologia (SFRH/BD/43984/2008). The Radio Frequency Identification (RFID) technology is a wireless technology that uses transmitted radio signals to tag, recognize, track and trace the movement of an item automatically. The study of this technology is actually considered a hot topic in all scientific areas and has been described as a major enabling technology for the automation of many processes. Although it is not a new technology it has only recently come to the awareness of the public and widely used in many sectors and particularly in the Healthcare.This paper aims to illustrate the deployment of RFID technology in Healthcare, more precisely in infant security systems. A case study about the experience of three hospitals and one RFID technology provider is presented to highlight the main architectural characteristics, functionality, and advantages associated to its deployment.After the case studies analysis it is possible to state that the infant security systems, using the RFID technology, are not so different among research case studies: they involve RFID tagging patients, they are easy to use not requiring an extensive training and also they are installed with an interface with others security systems. publishersversion published