Operations Research Applications in Health Care Management

Inhaltsverzeichnis

1. Frontmatter

2. Overview

   1. Frontmatter

   2. Chapter 1. A Taxonomy of Operations Research Studies in Healthcare Management

      Serhat Tüzün, Y. Ilker Topcu

      Abstract

      Operations Research has received considerable interest in recent years in the field of Healthcare Management Sciences. A wide variety of Operations Research techniques have been proposed in the literature. In this chapter, a taxonomy of Operations Research Methodology in Healthcare Management is presented in an attempt to provide a common terminology and a classification mechanism. Articles are classified to illustrate the descriptive power of this taxonomy.

   3. Chapter 2. A Comprehensive Survey on Healthcare Management

      Sezi Cevik Onar, Basar Oztaysi, Cengiz Kahraman

      Abstract

      Healthcare management (HCM) is the profession providing control and guidance to health service providers. Numerous quantitative and qualitative techniques including operations research (OR) techniques, statistical analyses, multi-criteria decision making techniques, etc. have been applied to healthcare problems. This chapter summarizes these techniques using a classification and presents graphical analyses of the survey results.

3. Medical Units in Hospital

   1. Frontmatter

   2. Chapter 3. The Real Time Management of Operating Rooms

      Davide Duma, Roberto Aringhieri

      Abstract

      At the operational decision level, the problem arising in the Operating Room (OR) planning is also called “surgery process scheduling”, which usually consists in selecting elective patients from a waiting list and assigning them to a specific operating room on a specific day, and determining the sequence of surgical procedures and the allocation of resources for each OR session. The Real Time Management (RTM) of operating rooms is the decision problem arising during the fulfillment of the surgery process scheduling, that is the problem of supervising the execution of such a schedule and, in case of delays, to take the more rational decision regarding the surgery cancellation or the overtime assignment. The RTM is characterized by the uncertainty of its main parameters, that is, for instance, the duration of a surgery and the arrivals of non-elective patients. In this chapter we propose online optimization approaches for the RTM capable to deal with (1) the elective and non-elective patient flows within a single surgical pathway (Non-Elective Worst Fit algorithm), and with (2) the resource sharing among different surgical pathways of elective patients (Flexible Overtime Allocation and Flexible Scheduling policies). We assess the effectiveness of the proposed solutions on simulated surgical clinical pathways under several scenarios. From a methodological point of view, our analysis suggested that online optimization can be a suitable methodology to deal with the inherent stochastic aspects arising in the majority of the health care problems.

   3. Chapter 4. Mixed Fuzzy Clustering for Deriving Predictive Models in Intensive Care Units

      Cátia M. Salgado, Susana M. Vieira, João M. C. Sousa

      Abstract

      This chapter presents two novel approaches for the identification of Takagi-Sugeno fuzzy models with time variant and time invariant features. The mixed fuzzy clustering (MFC) algorithm is used for determining the parameters of Takagi-Sugeno fuzzy models (FMs) in two different ways: (1) MFC FM, where the antecedent fuzzy sets are determined based on the partition matrix generated by the mixed fuzzy clustering algorithm; (2) FCM–U^MFC FM, where the input features are transformed using MFC and the antecedent fuzzy sets are derived using fuzzy c-means (FCM). The fuzzy modeling approaches are tested on four health care applications for the classification of critically ill patients: administration of vasopressors in pancreatitis and pneumonia patients, mortality in septic shock and early readmissions. Both approaches increase the performance of Takagi-Sugeno based on FCM, in all datasets. In particular, the best performer, FCM–U^MFC FM, achieves notable improvements in the four datasets.

   4. Chapter 5. Operations Research for Occupancy Modeling at Hospital Wards and Its Integration into Practice

      N. M. van de Vrugt, A. J. Schneider, M. E. Zonderland, D. A. Stanford, R. J. Boucherie

      Abstract

      In this chapter we review OR literature applied to hospital wards. Based on logistical characteristics and patient flow problems, we distinguish the following particular ward types: intensive care, acute medical units, obstetric wards, weekday wards, and general wards. We analyze typical trade-offs of performance measures for each ward type, and the common OR models applied to it. Additionally, we provide four modeling examples, discuss reported experiences with implementation of the research, and provide directions for future research. With this review we aim to guide both researchers and healthcare professionals in identifying which OR technique/model suits best for each specific hospital ward setting, and in actual implementation of the results in healthcare practice.

4. Care Process: Preparedness

   1. Frontmatter

   2. Chapter 6. Evaluating Healthcare System Efficiency of OECD Countries: A DEA-Based Study

      Zehra Önen, Serpil Sayın

      Abstract

      We evaluate healthcare system efficiency of 34 Organisation for Economic Cooperation and Development (OECD) member countries using Data Envelopment Analysis (DEA). Our implementations rely on data for the years 2008 and 2012. Our base model is an output oriented Banker-Charnes-Cooper model that uses the number of physicians, nurses, beds per 1000 population as inputs, and life expectancy at birth, infant survival rate as outputs. We observe that the distinction between efficient/inefficient countries is not necessarily in line with the classification of developed/developing countries, which is consistent with findings in past studies. We then build Assurance Region Global (ARG) versions of our base model in order to impose restrictions on weights assigned to outputs, thus limiting DEA’s inherent ability to interpret a decision making unit’s performance from a most optimistic point of view for that particular unit. We observe that some developing countries remain efficient in these models as well. Analysis of the results of our ARG model with 2008 data shows that Luxembourg appears as a reference for inefficient countries most frequently whereas Sweden, Chile and Spain appear in the reference list multiple times. In the 2012 ARG model, Slovenia appears most often as a reference followed up by Iceland and Spain. We then analyze efficiency with respect to the alternative output of survival from most likely causes of death such as ischemic heart disease, cerebrovascular diseases and malignant neoplasms (cancer) on 2008 and 2012 data. France is the model country for most of the inefficient countries with the new output variables. While there are some differences between the efficient countries of the base model and the modified one, the overlap is quite strong.

   3. Chapter 7. Healthcare Expenditure Prediction in Turkey by Using Genetic Algorithm Based Grey Forecasting Models

      Tuncay Özcan, Fatih Tüysüz

      Abstract

      This chapter aims to predict the health care expenditure (HCE) per capita which is an important indicator of a country’s health status and economic growth. Accurate estimation of HCE can guide efficient health care policy making and resource allocation. Grey forecasting models are applied for predicting the HCE per capita of Turkey. Three different strategies are proposed which are rolling mechanism, training data size optimization and parameter optimization to improve the forecasting accuracy of these models. Genetic algorithm (GA) which is one of the most widely used meta-heuristic optimization techniques is applied for training data size and parameter optimization of the grey forecasting models. The application results indicate that the optimization of parameters and training data size together with rolling mechanism highly improve the forecasting performance of the grey models.

   4. Chapter 8. The Impact of Social Networks in Developing and Managing Chronic Care Models

      Marina Resta, Elisabetta Arato

      Abstract

      The aim of this chapter is to monitor the impact of interrelations in the development of an efficient and proactive system of chronic care management by tools of Social Network Analysis (SNA). The basic motivation of this work resides in observing the complexity of the healthcare practice, embedded within an evolving framework with epidemiological changes, population ageing, and chronic diseases ruling on acute cases. Focusing on a population benchmark of patients affected by chronic degenerative disease, we provide evidence of how SNA can help to understand the dynamics of mutual relationships between the network of home-physicians and the other entities within the National Health System, and to suggest corrective interventions to improve the overall quality of the provided service.

5. Care Process: Precaution

   1. Frontmatter

   2. Chapter 9. Design and Planning of Organ Transplantation Networks

      Sahar Ahmadvand, Mir Saman Pishvaee

      Abstract

      Organ transplantation is one of the most successful therapy methods for many fatal diseases. As the demand for organs exceeds the supply, donated organs are considered as important and scarce national resources. Hence, planning for organ transplantation network in different levels including strategic, tactical and operational is an attractive and controversial research area with significant practical relevance. Strategic planning for organ transplantation network deals with long-term decisions (e.g., Transplant Center (TC) and Organ Procurement Organization (OPO) location problems and regional configuration design problem), while tactical/operational planning associates with the procurement of organs and short-term decisions (e.g., organ allocation and transportation of organs, patients and medical staff). Organ transplantation network planning is constrained by specific limitations due to the perishability of organs and emergent nature of transplantation activity which differentiate it from regular supply chains. This chapter aims to introduce and discuss the recent developments in organ transplantation network planning as well as presenting relevant case studies. In particular, this chapter focuses on mathematical programming and computational models proposed in the recent literature for organ transplantation network planning. Moreover, a systematic classification of the relevant literature as well as a number of attractive future research directions are provided for interested readers.

   3. Chapter 10. Blood Supply Chain Management and Future Research Opportunities

      Ali Ekici, Okan Örsan Özener, Elvin Çoban

      Abstract

      In this chapter, we discuss the challenges and research opportunities in the blood collection operations and explore the benefits of recent advances in the blood donation process. According to the regulations, donated blood has to be processed in a processing facility within 6 h of donation. This forces blood donation organizations to schedule continuous pickups from donation sites. The underlying mathematical problem is a variant of well-known Vehicle Routing Problem (VRP). The main differences are the perishability of the product to be collected, and the continuity of donations. We discuss the implications of such differences on collection routes from donation centers. Recent advances such as multicomponent apheresis (MCA) allow the donation of more than one component and/or more than one transfusable unit of each blood product. MCA provides several opportunities including (1) increasing the donor utilization, (2) tailoring the donations based on demand, and (3) reducing the infection risks in the transfusion. We also discuss MCA, its potential benefits and how to best use MCA in order to improve blood products availability and manage donation/disposal costs.

   4. Chapter 11. Vaccine Supply Management

      Nafiseh Shamsi G., S. Ali Torabi

      Abstract

      There are several causes such as natural or man-made disasters, malnutrition and environmental changes that may result in spread of infectious and communicable diseases which increases the risk of morbidity and mortality. Several control tools such as education and vaccination are adopted to prevent the spread of such epidemics. In this chapter, we focus on vaccination as a strong control tool and investigate the vaccine supply chains (VSCs) regarding some related challenges. The main problems of VSCs include vaccine sourcing, vaccine demand forecasting, vaccine shortages and the vaccine cold chains that suffer from the lack of mathematical modeling and precise solutions. Solving such problems may help the responsible public health organizations to improve the immunization coverage. Also, improving the efficiency and effectiveness of VSCs has a key role in a successful immunization program. Since stockpiling of vaccine supplies and VSC coordination are effective ways in on-time delivery of required vaccines and disease control interventions especially in the case of disasters and pandemics, we also review some related researches in VSC coordination contracts and appropriate stockpiling of necessary vaccines. The aim of this chapter is to classify some problems of the vaccine supply management, which has potential to be solved by the mathematical programming tools.

6. Care Process: Diagnosis and Prognosis

   1. Frontmatter

   2. Chapter 12. OR Applications in Disease Screening

      Evrim Didem Güneş, E. Lerzan Örmeci

      Abstract

      This chapter presents an overview of disease screening problems and operations research applications on different aspects of the problem. We first discuss operations research applications in evaluation and optimization of screening policies. Cost-effectiveness analysis and personalized medical decision making approaches for screening problems are be discussed here. The methodologies discussed include microsimulation models, compartmental models, general stochastic models and Partially Observed Markov Decision Processes. Then, organization of screening services for reaching out to the population and improving the effectiveness of screening, is discussed. Main topics included are location and resource allocation problems. We conclude with a brief discussion of future research directions.

   3. Chapter 13. Classification of Cancer Data: Analyzing Gene Expression Data Using a Fuzzy Decision Tree Algorithm

      Simone A. Ludwig, Stjepan Picek, Domagoj Jakobovic

      Abstract

      Decision tree algorithms are very popular in the area of data mining since the algorithms have a simple inference mechanism and provide a comprehensible way to represent the model. Over the past years, fuzzy decision tree algorithms have been proposed in order to handle the uncertainty in the data. Fuzzy decision tree algorithms have shown to outperform classical decision tree algorithms. This chapter investigates a fuzzy decision tree algorithm applied to the classification of gene expression data. The fuzzy decision tree algorithm is compared to a classical decision tree algorithm as well as other well-known data mining algorithms commonly applied to classification tasks. Based on the five data sets analyzed, the fuzzy decision tree algorithm outperforms the classical decision tree algorithm. However, compared to other commonly used classification algorithms, both decision tree algorithms are competitive, but they do not reach the accuracy values of the best performing classifier. One of the advantages of decision tree models including the fuzzy decision tree algorithm is however the simplicity and comprehensibility of the model as demonstrated in the chapter.

7. Care Process: Treatment

   1. Frontmatter

   2. Chapter 14. Efficiency of Diabetes Treatment

      Evidence from the UK Peter Wanke, Emel Aktas

      Abstract

      Diabetes is an emerging global epidemic linked to increases in physical inactivity, overweight, and obesity. The total number of deaths from diabetes is expected to rise by more than 50% in the next decade with a notable increase by more than 80% in upper-middle income countries. This paper proposes an integrated methodology to assess the efficiency of diabetes treatment and presents its application on data from the diabetes care providers in the UK. In this research, we use TOPSIS first in a two-stage approach to assess the relative efficiency of diabetes care providers. Then, in the second stage, we build neural networks and process TOPSIS results to construct a predictive model for diabetes treatment efficiency. The results reveal that variables related to hospital and patient demographics have a prominent impact on and predictive power for the efficiency assessment in diabetes treatment. Findings also indicate that the medical routines and treatment dynamics are quite standardized within different sites examined in this paper. To improve the efficiency of diabetes treatment, health care providers should focus on contextual variables such as prevalence of diabetes and management of diabetes.

   3. Chapter 15. A Multiobjective Solution Method for Radiation Treatment Planning

      Gokhan Kirlik, Serpil Sayın, Hao Howard Zhang

      Abstract

      The challenge in radiation treatment planning (RTP) is to ensure delivery of a prescription dose to the tumor while limiting the normal tissue toxicity. One way of dealing with this trade off is to use multiobjective optimization which no longer possesses a unique optimal objective function value. In multiobjective optimization, efficient solutions are used instead of the optimal solution which have the property that no improvement in any objective is possible without sacrificing in at least one other objective. In this study, we use achievement scalarization to obtain efficient solutions, i.e. treatment plans which are efficient, for the RTP. We adapt the parameters of the achievement scalarization to address a solution in a rectangle that is defined by the bounds on the objective functions. For a given set of bounds on each structure of the treatment volume, the formulation is able to attain a treatment plan that targets the bounds. We tested our approach on 10 locally advanced head-and-neck cancer cases. All of the cases include three tumor volumes, primary tumor, high-risk nodal volume, low-risk nodal volume, and five organs-at-risk (OAR), left and parotids, spinal cord, brain stem, oral cavity. We compare the proposed method with multiobjective solution algorithm from the literature and clinical plans. While satisfying the coverage of the target volumes, the proposed algorithm was able to improve the OAR sparing as much as 35%.

8. Medical Issues

   1. Frontmatter

   2. Chapter 16. OR Models for Emergency Medical Service (EMS) Management

      S. M. Gholami-Zanjani, M. S. Pishvaee, S. Ali Torabi

      Abstract

      Emergency Medical Service (EMS) systems provide pre-hospital medical assistance for those requiring prompt response and transportation. To realize such prompt response time at affordable cost, efficient planning of EMS is inevitable. Recently, these public safety systems have attracted considerable attention, since they provide remarkable services to people. Applying analytic techniques such as mathematical models and simulation models are becoming more common in emergency medical services. The intended mission of this chapter is to introduce and discuss the recent developments of Operations Research techniques for EMS management. Two selected mathematical models from the relevant literature are also elaborated. In addition, a real EMS location problem is described as a case study and finally a number of further research hints are presented.

   3. Chapter 17. Health Informatics

      I. Burak Parlak, A. Cağrı Tolga

      Abstract

      Multidisciplinary medical applications allow us to fill the gap between the engineering and the medicine. The new era in those applications leads to develop new tasks where engineering methods are reinterpreted with the medical point of view. Optimization is a critical tool in medical informatics where the data size is relatively higher and the decision time is relatively limited than other disciplines. Algorithmic developments integrate new problems within this topic using the optimization theory. In addition, decision making theory provides a better knowledge to tackle with macro level medical problems. Even if medical informatics is a broad topic, our focus is limited with the most challenging problems in this chapter. First of all, a brief survey of the related optimization procedures is introduced within the scope of medicine and engineering. The multidisciplinary tasks in medicine provides a complex follow up using the optimization theory. For this purpose, our study in medical informatics uses a development from the micro level; bioinformatics to the macro level; medical investments and hospital engineering. Furthermore, the sequence alignment problem is introduced through global and local techniques. Medical imaging is considered at the crucial level of preprocessing; segmentation and registration. Strategic medical unit reorganization is reviewed by multi criteria decision method. Finally, another cutting edge problem; medical device selection is resolved using favorable decision making. We concluded our study with future aspects and new trends.

   4. Chapter 18. OR Applications in Pharmaceutical Supply Chain Management

      Abbas Ahmadi, Mohammad Mousazadeh, S. Ali Torabi, Mir Saman Pishvaee

      Abstract

      Sustainable development of a nation greatly depends on the health of individuals. The emergence of the diseases caused by unhealthy lifestyle as well as the growth and aging of the population have faced the pharmaceutical industry with an increasing demand for drugs and the related products over time. This increase in demand has made the pharmaceutical industry as an important and large industry which constitutes a considerable portion of the healthcare expenditures. This sector is grappling with many challenges and inefficiencies in research and development activities, introducing new products, procurement, manufacturing, storage, and distribution affairs. Such issues have resulted in the inability of pharmaceutical companies to satisfy market demand in an efficient while effective manner. These problems alongside the dynamic and complex nature of a pharmaceutical supply chain (PSC) necessitate the employment of efficient optimization techniques to provide these companies with informed decision making by relying on available data. Hence, this chapter aims to identify the prevalent challenges of PSCs at three different decision levels, i.e., long-term (strategic), mid-term (tactical), and short-term (operational) decisions; as well as presenting various ways to deal with such problems. Accordingly, first, the characteristics of a PSC are presented and discussed. In order to provide a tangible perspective for application of Operations Research in PSC, an exhaustive mathematical programming model is presented. Then, a real practical case study is described and investigated and a number of avenues for further research are finally suggested.

   5. Chapter 19. A Categorical DEA Framework for Evaluating Medical Tourism Efficiency of “Top Destinations”

      Melis Almula Karadayi, Mine Isik

      Abstract

      Over the past decade, medical tourism has been one of the fastest growing sectors in developing countries. In this context, the attempt to monitor the performance of top medical tourism destinations has become a major concern. In spite of the fact that medical tourism has a significantly direct impact on not only GDP but also foreign exchange generation, there is an absence of empirical studies on the key drivers. This study proposes a categorical data envelopment analysis (DEA) framework for evaluating medical tourism performance of top destinations. Finally, research hypotheses are created to analyze the relationship between the countries’ medical tourism performance and their political, regulatory environment, technology and knowledge outputs.

9. Health Care Management

   1. Frontmatter

   2. Chapter 20. Healthcare Human Resource Planning

      John Pastor Ansah, Victoria Koh, Steffen Bayer, Paul Harper, David Matchar

      Abstract

      Healthcare human resource planning is an important aspect of health policy. Its importance arises in particular from the long time-delays, high-costs for training, and high proportion of healthcare expenditure allocated to it. Many countries experience workforce shortages in the healthcare sector, especially among nursing staff. This also has huge implications for population health; morbidity and mortality can increase in the face of inadequate healthcare human resources. Yet, the high degree of uncertainty related to policies, costs and patient behavior makes long-term planning a significant challenge. In this light, this chapter will discuss different analytical techniques used in healthcare human resource planning. Two case studies are presented to provide examples of real-world applications across different institutional context. One employs a systems methodology, while the other uses a linear programming method. Specifically, they aim to demonstrate the importance of adequate planning, and the various elements that need to be accounted for when planning healthcare human resources.

   3. Chapter 21. Lean Healthcare

      S. Ali Torabi, Shirin Haddad Pour, Nafiseh Shamsi G.

      Abstract

      Ever since the concept of lean was introduced in the manufacturing environment in Toyota Company; the ever-growing applications of this technique in other fields has been the subject of many studies. Healthcare environment is one of such fields in which the applications of lean tools are increasingly studied. In this chapter, the lean management techniques, their applications in the healthcare systems and how they can improve the performance of these systems by providing better patient service, better utilization of assets, and better patient care; are discussed. Furthermore, operational and strategic challenges faced in this sector are introduced. Additionally, the application of operations research tools in lean healthcare is explained. Finally, the practicality of lean healthcare and utilizing the lean concepts and tools in the healthcare sector is demonstrated through reviewing some case studies.

   4. Chapter 22. Procurement Management in Healthcare Systems

      Abbas Ahmadi, Mir Saman Pishvaee, S. Ali Torabi

      Abstract

      The healthcare industry is viewed as the world’s largest industry in terms of budget, employees, customers, etc. This industry always faces many challenges such as high inflation in healthcare costs, waste of time and resources when supplying the required items and failure to safeguard the availability of supplies. These prevalent issues have imposed various extreme political and public pressures on the healthcare providers. As it is obvious, such challenges greatly depend on the efficiency and effectiveness of procurement processes in the healthcare systems. Procurement management is traditionally considered as the most valuable part of supply chain management affecting many aspects of organizational performance. This fact is much more pronounced in the healthcare sector. Hereupon, the healthcare system needs to provide an efficient management of procurement affairs through efficient decision making techniques (e.g. optimization methods), instead of inert traditional approaches. This chapter introduces the procurement management in healthcare systems and the related challenges as well as some optimization approaches, which have been used to deal with procurement management problems in healthcare systems. Finally, a real practical case study is described and investigated.

10. Backmatter