International series in management science/operations research, 2021
The use of general descriptive names, registered names, trademarks, service marks, etc. in this p... more The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.
We present a methodology to design appointment systems for outpatient clinics and diagnostic faci... more We present a methodology to design appointment systems for outpatient clinics and diagnostic facilities that offer both walk-in and scheduled service. The developed blueprint for the appointment schedule prescribes the number of appointments to plan per day and the moment on the day to schedule the appointments. The method consists of two models that are linked by an algorithm; one for the day process that governs scheduled and unscheduled arrivals on the day and one for the access process of scheduled arrivals. Appointment schedules that balance the waiting time at the facility for unscheduled patients and the access time for scheduled patients, are calculated iteratively using the outcomes of the two models. The method is of general nature and can therefore also be applied to scheduling problems in other sectors than health care.
Memorandum (institute of Pacific Relations, American Council), Jun 1, 2011
We provide a comprehensive overview of the typical decisions to be made in resource capacity plan... more We provide a comprehensive overview of the typical decisions to be made in resource capacity planning and control in health care, and a structured review of relevant articles from the field of Operations Research and Management Sciences (OR/MS) for each planning decision. The contribution of this paper is twofold. First, to position the planning decisions, a taxonomy is presented. This taxonomy provides health care managers and OR/MS researchers with a method to identify, break down and classify planning and control decisions. Second, following the taxonomy, for six health care services, we provide an exhaustive specification of planning and control decisions in resource capacity planning and control. For each planning and control decision, we structurally review the key OR/MS articles and the OR/MS methods and techniques that are applied in the literature to support decision making.
We provide a comprehensive overview of the typical decisions to be made in resource capacity plan... more We provide a comprehensive overview of the typical decisions to be made in resource capacity planning and control in health care, and a structured review of relevant articles from the field of Operations Research and Management Sciences (OR/MS) for each planning decision. The contribution of this paper is twofold. First, to position the planning decisions, a taxonomy is presented. This taxonomy provides health care managers and OR/MS researchers with a method to identify, break down and classify planning and control decisions. Second, following the taxonomy, for six health care services, we provide an exhaustive specification of planning and control decisions in resource capacity planning and control. For each planning and control decision, we structurally review the key OR/MS articles and the OR/MS methods and techniques that are applied in the literature to support decision making.
This handbook provides our take on optimization of logistics processes in healthcare and on the g... more This handbook provides our take on optimization of logistics processes in healthcare and on the gap that exists between theory and practice. We will bridge that gap as all theoretical results presented in this book have actually been implemented in the healthcare domain. We are driven by a desire to improve the healthcare system, by effectively making an impact with Operations Research (OR). We discuss specific projects that have addressed major challenges for healthcare Operations Research. We present our solution approaches, our approaches to implement the results in practice, and the impact on healthcare organizations. In addition, we discuss the problems we encountered when implementing the results in practice and how we addressed them. In this introductory chapter, we discuss the ecosystem of our research center CHOIR (Center for Healthcare Operations Improvement & Research) and demonstrate how we have an impact on healthcare logistics
This handbook provides our take on optimization of logistics processes in healthcare and on the g... more This handbook provides our take on optimization of logistics processes in healthcare and on the gap that exists between theory and practice. We will bridge that gap as all theoretical results presented in this book have actually been implemented in the healthcare domain. We are driven by a desire to improve the healthcare system, by effectively making an impact with Operations Research (OR). We discuss specific projects that have addressed major challenges for healthcare Operations Research. We present our solution approaches, our approaches to implement the results in practice, and the impact on healthcare organizations. In addition, we discuss the problems we encountered when implementing the results in practice and how we addressed them. In this introductory chapter, we discuss the ecosystem of our research center CHOIR (Center for Healthcare Operations Improvement & Research) and demonstrate how we have an impact on healthcare logistics
International Series in Operations Research & Management Science, 2021
This handbook provides our take on optimization of logistics processes in healthcare and on the g... more This handbook provides our take on optimization of logistics processes in healthcare and on the gap that exists between theory and practice. We will bridge that gap as all theoretical results presented in this book have actually been implemented in the healthcare domain. We are driven by a desire to improve the healthcare system, by effectively making an impact with Operations Research (OR). We discuss specific projects that have addressed major challenges for healthcare Operations Research. We present our solution approaches, our approaches to implement the results in practice, and the impact on healthcare organizations. In addition, we discuss the problems we encountered when implementing the results in practice and how we addressed them. In this introductory chapter, we discuss the ecosystem of our research center CHOIR (Center for Healthcare Operations Improvement & Research) and demonstrate how we have an impact on healthcare logistics
International Journal of Production Economics, 2015
Workload on nursing wards depends highly on patient arrivals and patient lengths of stay, which a... more Workload on nursing wards depends highly on patient arrivals and patient lengths of stay, which are both inherently variable. Predicting this workload and staffing nurses accordingly is essential for guaranteeing quality of care in a cost effective manner. This paper introduces a stochastic method that uses hourly census predictions to derive efficient nurse staffing policies. The generic analytic approach minimizes staffing levels while satisfying so-called nurse-to-patient ratios. In particular, we explore the potential of flexible staffing policies which allow hospitals to dynamically respond to their fluctuating patient population by employing float nurses. The method is applied to a case study of the surgical inpatient clinic of the Academic Medical Center (AMC) Amsterdam. This case study demonstrates the method's potential to study the complex interaction between staffing requirements and several interrelated planning issues such as case mix, care unit partitioning and size, and surgical block planning. Inspired by the numerical results, the AMC decided that this flexible nurse staffing methodology will be incorporated in the redesign of the inpatient care operations during the upcoming years.
Enabling patients to walk in for their diagnostic examination without an appointment has consider... more Enabling patients to walk in for their diagnostic examination without an appointment has considerable potential in terms of quality of care, patient service, and system efficiency. We present a model to evaluate the effect of implementing a combined walk-in and appointment system, offering appointments to all patients preferring or strictly requiring these, while enabling all other patients to walk in. In a combined system, appointments can be scheduled in periods with low walk-in demand to counterbalance the possible high variability in walk-in arrival rates. We develop a discrete event simulation model, combined with an intelligent algorithmic methodology for appointment schedule optimization, for evaluating the implementation of a combined walk-in and appointment system for diagnostic examinations. Our simulation model is reusable: its component-based structure and generic underlying logic enable it to automatically represent any type of diagnostic facility, for which it can then evaluate the effect of implementing a combined walk-in and appointment system. Applying this approach, we quantitatively investigate the impact of implementing a combined walk-in and appointment system for CT-scans, performing a case study at the Academic Medical Center (AMC) Amsterdam. Inspired by the results, the AMC CT-facility has implemented a combined walk-in and appointment system, thereby shortening patients' diagnostic trajectories, and decreasing the number of required hospital visits for many patients.
The Academic Medical Center (AMC) in Amsterdam, The Netherlands, recently opened the 'Children's ... more The Academic Medical Center (AMC) in Amsterdam, The Netherlands, recently opened the 'Children's Muscle Center Amsterdam' (CMCA). The CMCA diagnoses and treats children with neuromuscular diseases. The patients with such diseases require care from a variety of clinicians. Through the establishment of the CMCA, children and their parents will generally visit the hospital only once a year, while previously they used to visit on average six times a year. This is a major improvement, because the hospital visits are both physically and psychologically demanding for the patients. This paper describes how quantitative modelling supports the design and operations of the CMCA. First, an integer linear program is presented that selects which patients are to be invited for a treatment day and schedules the required combination of consultations, examinations and treatments on one day. Second, the integer linear program is used as input to a simulation study to estimate the capacity of the CMCA, expressed in terms of the distribution of the number patients that can be seen on one diagnosis day. Finally, a queueing model is formulated to predict the access time distributions based upon the simulation outcomes under various demand scenarios. Its contribution on the case under study is twofold. First, we design highly constrained appointment schedules for multiple patients that require service from multiple disciplines' resources. Second, we study the effect of the trade-offs between scheduling constraints and access times. As such, the contribution of this case study paper is that it illustrates the value of applying Operations Research techniques in complex healthcare settings, by designing context-specific combinations of mathematical models, thereby improving delivery of the highly-constrained multidisciplinary care.
This paper presents a methodology to plan treatments for rehabilitation outpatients. These patien... more This paper presents a methodology to plan treatments for rehabilitation outpatients. These patients require a series of treatments by therapists from various disciplines. In current practice, when treatments are planned, a lack of coordination between the different disciplines, along with a failure to plan the entire treatment plan at once, often occurs. This situation jeopardizes both the quality of care and the logistical performance.
Research Motivation and Outline During the upcoming decades, healthcare organizations face the ch... more Research Motivation and Outline During the upcoming decades, healthcare organizations face the challenge to deliver more patient care, of higher quality, and with less financial and human resources. The goal of this dissertation is, by developing operations research techniques, to help and guide healthcare professionals making their organizations future-proof. 1.1 Healthcare in the 21st century During the 20th century, healthcare delivery has contributed to a striking worldwide health improvement. Despite its unmistakable benefits, the healthcare sector is under serious strain [466, 639]. Demand for and expenditures on healthcare increase steadily, as a result of ageing populations, technological developments, and increased medical knowledge. At the same time, patient expectations, competition between healthcare organizations, and labor shortages are rising. A joint effort is required by policy-makers, insurers, and care providers to fundamentally reconsider the way healthcare is delivered. Since 1960, life expectancy has increased on average across countries of the OECD (Organisation of Economic Cooperation and Development) by more than 11 years, reaching nearly 80 years in 2009 [466]. Concurrently, the past 50 years have shown a steady rise in healthcare spending, which has tended to grow faster than Gross Domestic Products (GDP). In 1960, health spending among health systems in OECD countries accounted for under 4% of GDP on average. By 2009, this had risen to 9.6%, with many countries spending over 10% of GDP. Particularly in the United States, the health spending share of GDP grew rapidly from about 5% in 1960 to over 17% in 2009. The next highest country, allocating 12%, was the Netherlands. The Netherlands is a striking example of a country facing tremendous healthcare challenges. The Dutch government is convinced of the urgency of the problem [445]. Without drastic measures being taken, the Netherlands Bureau for Economic Policy Analysis (CPB) predicts that the health spending share of GDP potentially grows to more than 30% in 2040 (see Figure 1.1) [109]. With more people demanding care and a workforce that is not expected to grow in size, the share of the working population employed in the healthcare sector is expected to increase sharply (see Figure 1.2). These developments will put under pressure other areas that drive society, like education, social security, and environmental welfare. CHAPTER 2 Structured Review of the State of the Art in Operations Research 2.1 Introduction In Chapter 1, resource capacity planning and control in healthcare, and Operations Research and Management Sciences (OR/MS) were introduced and defined. In the current chapter, we provide a structured overview of the typical decisions to be made in resource capacity planning and control in healthcare, and we provide a review of the relevant OR/MS literature for each planning decision. First, a taxonomy is formulated to identify and position planning and control decisions. This taxonomy is the starting point to obtain a complete specification of planning decisions, and to gain understanding of the interrelations between various planning decisions. Herewith, we aim to guide healthcare professionals and OR/MS researchers through the broad field of OR/MS in healthcare. On the one hand, healthcare professionals can identify lacking, insufficiently defined and incoherent planning decisions within their department or organization. On the other hand, it gives the opportunity to identify decisions that are not yet addressed often in the OR/MS literature. The contribution of this chapter is twofold. First, to position the planning decisions, we present a taxonomy. This taxonomy provides healthcare managers and OR/MS researchers with a method to identify, break down and classify planning and control decisions. The taxonomy contains two axes. The vertical axis reflects the hierarchical nature of decision making in resource capacity planning and control, and the horizontal axis the various healthcare services. The vertical axis is strongly connected, because higher-level decisions demarcate the scope of and impose restrictions on lower-level decisions. Although healthcare delivery is generally organized in autonomous organizations and departments, the horizontal axis is also strongly interrelated as a patient pathway often consists of several healthcare services from multiple organizations or departments. Second, following the vertical axis of the taxonomy, and for each healthcare service on the horizontal axis, we provide a comprehensive specification of planning and control decisions in resource capacity planning and control. For each planning and control decision, we structurally review the key OR/MS articles and the OR/MS techniques that are applied in the literature to support decision making. No struc-Chapter 2. Structured Review of the State of the Art in Operations Research tured review exists of this nature, as existing reviews are typically exhaustive within a confined scope, such as simulation modeling in healthcare [339] or outpatient appointment scheduling [104], or are more general to the extent that they do not focus on the concrete specific decisions. This chapter is organized as follows. Section 2.2 presents the taxonomy. Section 2.3 states the objectives, defines the scope, and summarizes the search method for the literature review. With the taxonomy as the foundation, Sections 2.4-2.6 identify, classify and discuss the planning and control decisions. Section 2.7 concludes this chapter with a discussion of our findings. 2.2. Taxonomy interrelation between hierarchical levels. Top-down interaction exists as higher-level decisions demarcate the scope of and impose restrictions on lower-level decisions. Conversely, bottom-up interaction exists as feedback about the healthcare delivery realization supports decision making in higher levels. Horizontal axis On the horizontal axis of our taxonomy we position the different services in healthcare. The complete spectrum of healthcare delivery is a composition of many different services provided by many different organizations. From the perspective of resource capacity planning and control, different services may face similar questions. To capture this similarity, we distinguish six clusters of healthcare services. The definitions of the six care services are obtained from the corresponding MeSH terms provided by PubMed [574]. For each care service we offer several examples of facilities that provide this service. Ambulatory care services provide care to patients without offering a room, a bed and board, and they may be free-standing or part of a hospital. Examples of ambulatory care facilities are outpatient clinics, primary care services and the hospital departments of endoscopy, radiology and radiotherapy. Emergency care services are concerned with the evaluation and initial treatment of urgent and emergent medical problems, such as those caused by accidents, trauma, sudden illness, poisoning, or disasters. Emergency medical care can be provided at the hospital or at sites outside the medical facility. Examples of emergency care facilities are hospital emergency departments, ambulances and trauma centers. Surgical care services provide operative procedures (surgeries) for the correction of deformities and defects, repair of injuries, and diagnosis and cure of certain diseases. Examples of surgical care facilities are the hospital's operating theater, surgical daycare centers and anesthesia facilities. Inpatient care services provide care to hospitalized patients by offering a room, a bed and board. Examples are intensive care units, general nursing wards, and neonatal care units. Home care services are community health and nursing services that provide multiple, coordinated services to a patient at the patient's home. Home care services are provided by a visiting nurse, home health agencies, hospitals, or organized community groups using professional staff for healthcare delivery. Examples are medical care at home, housekeeping support and personal hygiene assistance. Residential care services provide supervision and assistance in activities of daily living with medical and nursing services when required. Examples are nursing homes, psychiatric hospitals, rehabilitation clinics with overnight stay, homes for the aged, and hospices. 2.4. Ambulatory care services to the geographical location of demand [181]. The main trade-off in this decision is between patient accessibility and efficiency. Patient accessibility is represented by access time and travel distance indicators. Efficiency is represented by utilization and productivity indicators [181, 540]. Common regional planning models incorporate the dependency of demand on the regional demographic and socioeconomic characteristics [2]. Methods: computer simulation [425, 505, 543, 562], heuristics [2, 181], literature review [540]. Service mix. An organization decides the particular services that the ambulatory care facility provides. The service mix stipulates which patient types can be consulted. In general, the service mix decision is not made at an ambulatory care service level, but at the regional or hospital level, as it integrally impacts the ambulatory, emergency, surgical and inpatient care services. This is also expressed by [606] in which for example inpatient resources, such as beds and nursing staff, are indicated as 'following' resources. This may be the reason that we have not found any papers focusing on service mix decisions for ambulatory care services in specific. Methods: no papers found. Case mix. Every ambulatory care facility decides on a particular case mix, which is the volume and composition of patient groups that the facility serves. The settled service mix restricts the decisions to serve particular patient groups. Patient groups can be classified based on disease type, age, acuteness, home address,...
This chapter aims to promote and illustrate the fruitful combination of classical Operations Rese... more This chapter aims to promote and illustrate the fruitful combination of classical Operations Research (OR) and Computer Simulation. First, a highly instructive example of parallel queues will be studied. This simple example already shows the necessary combination of OR (queueing) and simulation that appears to be of practical interest such as for call center optimization. Next, two more 'real life' applications are regarded: • blood platelet production and inventory management at blood banks, and • train conflict resolution for railway junctions. Both applications show the useful combination of Simulation and optimization methods from OR, in particular Stochastic Dynamic Programming (SDP) and Markov decision theory (MDP), to obtain simple rules that are nearly optimal. The results are based on real life Dutch case studies and show that this combined OR-Simulation approach can be most useful for 'practical optimization' and that it is still wide open for further application.
We present a methodology to design appointment systems for outpatient clinics and diagnostic faci... more We present a methodology to design appointment systems for outpatient clinics and diagnostic facilities that offer both walk-in and scheduled service. The developed blueprint for the appointment schedule prescribes the number of appointments to plan per day and the moment on the day to schedule the appointments. The method consists of two models that are linked by an algorithm; one for the day process that governs scheduled and unscheduled arrivals on the day and one for the access process of scheduled arrivals. Appointment schedules that balance the waiting time at the facility for unscheduled patients and the access time for scheduled patients, are calculated iteratively using the outcomes of the two models. The method is of general nature and can therefore also be applied to scheduling problems in other sectors than health care.
International series in management science/operations research, 2021
The use of general descriptive names, registered names, trademarks, service marks, etc. in this p... more The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.
We present a methodology to design appointment systems for outpatient clinics and diagnostic faci... more We present a methodology to design appointment systems for outpatient clinics and diagnostic facilities that offer both walk-in and scheduled service. The developed blueprint for the appointment schedule prescribes the number of appointments to plan per day and the moment on the day to schedule the appointments. The method consists of two models that are linked by an algorithm; one for the day process that governs scheduled and unscheduled arrivals on the day and one for the access process of scheduled arrivals. Appointment schedules that balance the waiting time at the facility for unscheduled patients and the access time for scheduled patients, are calculated iteratively using the outcomes of the two models. The method is of general nature and can therefore also be applied to scheduling problems in other sectors than health care.
Memorandum (institute of Pacific Relations, American Council), Jun 1, 2011
We provide a comprehensive overview of the typical decisions to be made in resource capacity plan... more We provide a comprehensive overview of the typical decisions to be made in resource capacity planning and control in health care, and a structured review of relevant articles from the field of Operations Research and Management Sciences (OR/MS) for each planning decision. The contribution of this paper is twofold. First, to position the planning decisions, a taxonomy is presented. This taxonomy provides health care managers and OR/MS researchers with a method to identify, break down and classify planning and control decisions. Second, following the taxonomy, for six health care services, we provide an exhaustive specification of planning and control decisions in resource capacity planning and control. For each planning and control decision, we structurally review the key OR/MS articles and the OR/MS methods and techniques that are applied in the literature to support decision making.
We provide a comprehensive overview of the typical decisions to be made in resource capacity plan... more We provide a comprehensive overview of the typical decisions to be made in resource capacity planning and control in health care, and a structured review of relevant articles from the field of Operations Research and Management Sciences (OR/MS) for each planning decision. The contribution of this paper is twofold. First, to position the planning decisions, a taxonomy is presented. This taxonomy provides health care managers and OR/MS researchers with a method to identify, break down and classify planning and control decisions. Second, following the taxonomy, for six health care services, we provide an exhaustive specification of planning and control decisions in resource capacity planning and control. For each planning and control decision, we structurally review the key OR/MS articles and the OR/MS methods and techniques that are applied in the literature to support decision making.
This handbook provides our take on optimization of logistics processes in healthcare and on the g... more This handbook provides our take on optimization of logistics processes in healthcare and on the gap that exists between theory and practice. We will bridge that gap as all theoretical results presented in this book have actually been implemented in the healthcare domain. We are driven by a desire to improve the healthcare system, by effectively making an impact with Operations Research (OR). We discuss specific projects that have addressed major challenges for healthcare Operations Research. We present our solution approaches, our approaches to implement the results in practice, and the impact on healthcare organizations. In addition, we discuss the problems we encountered when implementing the results in practice and how we addressed them. In this introductory chapter, we discuss the ecosystem of our research center CHOIR (Center for Healthcare Operations Improvement & Research) and demonstrate how we have an impact on healthcare logistics
This handbook provides our take on optimization of logistics processes in healthcare and on the g... more This handbook provides our take on optimization of logistics processes in healthcare and on the gap that exists between theory and practice. We will bridge that gap as all theoretical results presented in this book have actually been implemented in the healthcare domain. We are driven by a desire to improve the healthcare system, by effectively making an impact with Operations Research (OR). We discuss specific projects that have addressed major challenges for healthcare Operations Research. We present our solution approaches, our approaches to implement the results in practice, and the impact on healthcare organizations. In addition, we discuss the problems we encountered when implementing the results in practice and how we addressed them. In this introductory chapter, we discuss the ecosystem of our research center CHOIR (Center for Healthcare Operations Improvement & Research) and demonstrate how we have an impact on healthcare logistics
International Series in Operations Research & Management Science, 2021
This handbook provides our take on optimization of logistics processes in healthcare and on the g... more This handbook provides our take on optimization of logistics processes in healthcare and on the gap that exists between theory and practice. We will bridge that gap as all theoretical results presented in this book have actually been implemented in the healthcare domain. We are driven by a desire to improve the healthcare system, by effectively making an impact with Operations Research (OR). We discuss specific projects that have addressed major challenges for healthcare Operations Research. We present our solution approaches, our approaches to implement the results in practice, and the impact on healthcare organizations. In addition, we discuss the problems we encountered when implementing the results in practice and how we addressed them. In this introductory chapter, we discuss the ecosystem of our research center CHOIR (Center for Healthcare Operations Improvement & Research) and demonstrate how we have an impact on healthcare logistics
International Journal of Production Economics, 2015
Workload on nursing wards depends highly on patient arrivals and patient lengths of stay, which a... more Workload on nursing wards depends highly on patient arrivals and patient lengths of stay, which are both inherently variable. Predicting this workload and staffing nurses accordingly is essential for guaranteeing quality of care in a cost effective manner. This paper introduces a stochastic method that uses hourly census predictions to derive efficient nurse staffing policies. The generic analytic approach minimizes staffing levels while satisfying so-called nurse-to-patient ratios. In particular, we explore the potential of flexible staffing policies which allow hospitals to dynamically respond to their fluctuating patient population by employing float nurses. The method is applied to a case study of the surgical inpatient clinic of the Academic Medical Center (AMC) Amsterdam. This case study demonstrates the method's potential to study the complex interaction between staffing requirements and several interrelated planning issues such as case mix, care unit partitioning and size, and surgical block planning. Inspired by the numerical results, the AMC decided that this flexible nurse staffing methodology will be incorporated in the redesign of the inpatient care operations during the upcoming years.
Enabling patients to walk in for their diagnostic examination without an appointment has consider... more Enabling patients to walk in for their diagnostic examination without an appointment has considerable potential in terms of quality of care, patient service, and system efficiency. We present a model to evaluate the effect of implementing a combined walk-in and appointment system, offering appointments to all patients preferring or strictly requiring these, while enabling all other patients to walk in. In a combined system, appointments can be scheduled in periods with low walk-in demand to counterbalance the possible high variability in walk-in arrival rates. We develop a discrete event simulation model, combined with an intelligent algorithmic methodology for appointment schedule optimization, for evaluating the implementation of a combined walk-in and appointment system for diagnostic examinations. Our simulation model is reusable: its component-based structure and generic underlying logic enable it to automatically represent any type of diagnostic facility, for which it can then evaluate the effect of implementing a combined walk-in and appointment system. Applying this approach, we quantitatively investigate the impact of implementing a combined walk-in and appointment system for CT-scans, performing a case study at the Academic Medical Center (AMC) Amsterdam. Inspired by the results, the AMC CT-facility has implemented a combined walk-in and appointment system, thereby shortening patients' diagnostic trajectories, and decreasing the number of required hospital visits for many patients.
The Academic Medical Center (AMC) in Amsterdam, The Netherlands, recently opened the 'Children's ... more The Academic Medical Center (AMC) in Amsterdam, The Netherlands, recently opened the 'Children's Muscle Center Amsterdam' (CMCA). The CMCA diagnoses and treats children with neuromuscular diseases. The patients with such diseases require care from a variety of clinicians. Through the establishment of the CMCA, children and their parents will generally visit the hospital only once a year, while previously they used to visit on average six times a year. This is a major improvement, because the hospital visits are both physically and psychologically demanding for the patients. This paper describes how quantitative modelling supports the design and operations of the CMCA. First, an integer linear program is presented that selects which patients are to be invited for a treatment day and schedules the required combination of consultations, examinations and treatments on one day. Second, the integer linear program is used as input to a simulation study to estimate the capacity of the CMCA, expressed in terms of the distribution of the number patients that can be seen on one diagnosis day. Finally, a queueing model is formulated to predict the access time distributions based upon the simulation outcomes under various demand scenarios. Its contribution on the case under study is twofold. First, we design highly constrained appointment schedules for multiple patients that require service from multiple disciplines' resources. Second, we study the effect of the trade-offs between scheduling constraints and access times. As such, the contribution of this case study paper is that it illustrates the value of applying Operations Research techniques in complex healthcare settings, by designing context-specific combinations of mathematical models, thereby improving delivery of the highly-constrained multidisciplinary care.
This paper presents a methodology to plan treatments for rehabilitation outpatients. These patien... more This paper presents a methodology to plan treatments for rehabilitation outpatients. These patients require a series of treatments by therapists from various disciplines. In current practice, when treatments are planned, a lack of coordination between the different disciplines, along with a failure to plan the entire treatment plan at once, often occurs. This situation jeopardizes both the quality of care and the logistical performance.
Research Motivation and Outline During the upcoming decades, healthcare organizations face the ch... more Research Motivation and Outline During the upcoming decades, healthcare organizations face the challenge to deliver more patient care, of higher quality, and with less financial and human resources. The goal of this dissertation is, by developing operations research techniques, to help and guide healthcare professionals making their organizations future-proof. 1.1 Healthcare in the 21st century During the 20th century, healthcare delivery has contributed to a striking worldwide health improvement. Despite its unmistakable benefits, the healthcare sector is under serious strain [466, 639]. Demand for and expenditures on healthcare increase steadily, as a result of ageing populations, technological developments, and increased medical knowledge. At the same time, patient expectations, competition between healthcare organizations, and labor shortages are rising. A joint effort is required by policy-makers, insurers, and care providers to fundamentally reconsider the way healthcare is delivered. Since 1960, life expectancy has increased on average across countries of the OECD (Organisation of Economic Cooperation and Development) by more than 11 years, reaching nearly 80 years in 2009 [466]. Concurrently, the past 50 years have shown a steady rise in healthcare spending, which has tended to grow faster than Gross Domestic Products (GDP). In 1960, health spending among health systems in OECD countries accounted for under 4% of GDP on average. By 2009, this had risen to 9.6%, with many countries spending over 10% of GDP. Particularly in the United States, the health spending share of GDP grew rapidly from about 5% in 1960 to over 17% in 2009. The next highest country, allocating 12%, was the Netherlands. The Netherlands is a striking example of a country facing tremendous healthcare challenges. The Dutch government is convinced of the urgency of the problem [445]. Without drastic measures being taken, the Netherlands Bureau for Economic Policy Analysis (CPB) predicts that the health spending share of GDP potentially grows to more than 30% in 2040 (see Figure 1.1) [109]. With more people demanding care and a workforce that is not expected to grow in size, the share of the working population employed in the healthcare sector is expected to increase sharply (see Figure 1.2). These developments will put under pressure other areas that drive society, like education, social security, and environmental welfare. CHAPTER 2 Structured Review of the State of the Art in Operations Research 2.1 Introduction In Chapter 1, resource capacity planning and control in healthcare, and Operations Research and Management Sciences (OR/MS) were introduced and defined. In the current chapter, we provide a structured overview of the typical decisions to be made in resource capacity planning and control in healthcare, and we provide a review of the relevant OR/MS literature for each planning decision. First, a taxonomy is formulated to identify and position planning and control decisions. This taxonomy is the starting point to obtain a complete specification of planning decisions, and to gain understanding of the interrelations between various planning decisions. Herewith, we aim to guide healthcare professionals and OR/MS researchers through the broad field of OR/MS in healthcare. On the one hand, healthcare professionals can identify lacking, insufficiently defined and incoherent planning decisions within their department or organization. On the other hand, it gives the opportunity to identify decisions that are not yet addressed often in the OR/MS literature. The contribution of this chapter is twofold. First, to position the planning decisions, we present a taxonomy. This taxonomy provides healthcare managers and OR/MS researchers with a method to identify, break down and classify planning and control decisions. The taxonomy contains two axes. The vertical axis reflects the hierarchical nature of decision making in resource capacity planning and control, and the horizontal axis the various healthcare services. The vertical axis is strongly connected, because higher-level decisions demarcate the scope of and impose restrictions on lower-level decisions. Although healthcare delivery is generally organized in autonomous organizations and departments, the horizontal axis is also strongly interrelated as a patient pathway often consists of several healthcare services from multiple organizations or departments. Second, following the vertical axis of the taxonomy, and for each healthcare service on the horizontal axis, we provide a comprehensive specification of planning and control decisions in resource capacity planning and control. For each planning and control decision, we structurally review the key OR/MS articles and the OR/MS techniques that are applied in the literature to support decision making. No struc-Chapter 2. Structured Review of the State of the Art in Operations Research tured review exists of this nature, as existing reviews are typically exhaustive within a confined scope, such as simulation modeling in healthcare [339] or outpatient appointment scheduling [104], or are more general to the extent that they do not focus on the concrete specific decisions. This chapter is organized as follows. Section 2.2 presents the taxonomy. Section 2.3 states the objectives, defines the scope, and summarizes the search method for the literature review. With the taxonomy as the foundation, Sections 2.4-2.6 identify, classify and discuss the planning and control decisions. Section 2.7 concludes this chapter with a discussion of our findings. 2.2. Taxonomy interrelation between hierarchical levels. Top-down interaction exists as higher-level decisions demarcate the scope of and impose restrictions on lower-level decisions. Conversely, bottom-up interaction exists as feedback about the healthcare delivery realization supports decision making in higher levels. Horizontal axis On the horizontal axis of our taxonomy we position the different services in healthcare. The complete spectrum of healthcare delivery is a composition of many different services provided by many different organizations. From the perspective of resource capacity planning and control, different services may face similar questions. To capture this similarity, we distinguish six clusters of healthcare services. The definitions of the six care services are obtained from the corresponding MeSH terms provided by PubMed [574]. For each care service we offer several examples of facilities that provide this service. Ambulatory care services provide care to patients without offering a room, a bed and board, and they may be free-standing or part of a hospital. Examples of ambulatory care facilities are outpatient clinics, primary care services and the hospital departments of endoscopy, radiology and radiotherapy. Emergency care services are concerned with the evaluation and initial treatment of urgent and emergent medical problems, such as those caused by accidents, trauma, sudden illness, poisoning, or disasters. Emergency medical care can be provided at the hospital or at sites outside the medical facility. Examples of emergency care facilities are hospital emergency departments, ambulances and trauma centers. Surgical care services provide operative procedures (surgeries) for the correction of deformities and defects, repair of injuries, and diagnosis and cure of certain diseases. Examples of surgical care facilities are the hospital's operating theater, surgical daycare centers and anesthesia facilities. Inpatient care services provide care to hospitalized patients by offering a room, a bed and board. Examples are intensive care units, general nursing wards, and neonatal care units. Home care services are community health and nursing services that provide multiple, coordinated services to a patient at the patient's home. Home care services are provided by a visiting nurse, home health agencies, hospitals, or organized community groups using professional staff for healthcare delivery. Examples are medical care at home, housekeeping support and personal hygiene assistance. Residential care services provide supervision and assistance in activities of daily living with medical and nursing services when required. Examples are nursing homes, psychiatric hospitals, rehabilitation clinics with overnight stay, homes for the aged, and hospices. 2.4. Ambulatory care services to the geographical location of demand [181]. The main trade-off in this decision is between patient accessibility and efficiency. Patient accessibility is represented by access time and travel distance indicators. Efficiency is represented by utilization and productivity indicators [181, 540]. Common regional planning models incorporate the dependency of demand on the regional demographic and socioeconomic characteristics [2]. Methods: computer simulation [425, 505, 543, 562], heuristics [2, 181], literature review [540]. Service mix. An organization decides the particular services that the ambulatory care facility provides. The service mix stipulates which patient types can be consulted. In general, the service mix decision is not made at an ambulatory care service level, but at the regional or hospital level, as it integrally impacts the ambulatory, emergency, surgical and inpatient care services. This is also expressed by [606] in which for example inpatient resources, such as beds and nursing staff, are indicated as 'following' resources. This may be the reason that we have not found any papers focusing on service mix decisions for ambulatory care services in specific. Methods: no papers found. Case mix. Every ambulatory care facility decides on a particular case mix, which is the volume and composition of patient groups that the facility serves. The settled service mix restricts the decisions to serve particular patient groups. Patient groups can be classified based on disease type, age, acuteness, home address,...
This chapter aims to promote and illustrate the fruitful combination of classical Operations Rese... more This chapter aims to promote and illustrate the fruitful combination of classical Operations Research (OR) and Computer Simulation. First, a highly instructive example of parallel queues will be studied. This simple example already shows the necessary combination of OR (queueing) and simulation that appears to be of practical interest such as for call center optimization. Next, two more 'real life' applications are regarded: • blood platelet production and inventory management at blood banks, and • train conflict resolution for railway junctions. Both applications show the useful combination of Simulation and optimization methods from OR, in particular Stochastic Dynamic Programming (SDP) and Markov decision theory (MDP), to obtain simple rules that are nearly optimal. The results are based on real life Dutch case studies and show that this combined OR-Simulation approach can be most useful for 'practical optimization' and that it is still wide open for further application.
We present a methodology to design appointment systems for outpatient clinics and diagnostic faci... more We present a methodology to design appointment systems for outpatient clinics and diagnostic facilities that offer both walk-in and scheduled service. The developed blueprint for the appointment schedule prescribes the number of appointments to plan per day and the moment on the day to schedule the appointments. The method consists of two models that are linked by an algorithm; one for the day process that governs scheduled and unscheduled arrivals on the day and one for the access process of scheduled arrivals. Appointment schedules that balance the waiting time at the facility for unscheduled patients and the access time for scheduled patients, are calculated iteratively using the outcomes of the two models. The method is of general nature and can therefore also be applied to scheduling problems in other sectors than health care.
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Papers by Nikky Kortbeek