Handbook of Healthcare Operations Management

Patient access to healthcare is a major problem area due to inadequate supplies and misallocation of resources including physicians, nurses, and hospital beds. Increasing patient demands due to an aging and more chronically ill population will exacerbate this situation, leading to longer delays for care, hurried treatment times, and adverse clinical outcomes. Though there is a significant operations literature focused on methods to mitigate these effects, suggested remedies may be ineffective due to adaptive behavior by both physicians and patients. This chapter will focus on the quantification and impact of such adaptive behavior on the ability to provide timely patient access to limited health services.

Patients seeking healthcare often need to wait before they can receive needed services. Excessive waiting can cause prolonged discomfort, economic loss, and long-run health complications. This motivates us to look closely at the theory of queues in order to understand the reasons why queues form and the principles underlying good system design. Queueing models help explain the interaction between resource utilization and variability. Higher resource utilization lowers the per-patient cost of making resources available, but in the presence of variability in either the service requirements or the number of service requests or both, higher utilization increases patient waiting times. In fact, for a fixed level of variability, the effect of resource utilization is highly nonlinear—waiting times increase at an increasing rate in utilization. This implies that in healthcare settings where significant variability is naturally present and difficult to eliminate, capacity planning must trade-off the cost of providing resources and the cost of patient waiting. In this chapter, we review basic queueing models that help quantify the above-mentioned tradeoff and discuss the usefulness of such models to healthcare operations managers. Specifically, we summarize some known results for queueing systems with single and multiple servers, limited and unlimited waiting room, service priority, and networks of service stations.

Agent-based modeling (ABM) is rapidly gaining momentum in many fields, and it has added to the insights previously contributed by other modeling and simulation methods such as system dynamics and discrete event simulation. Healthcare operations management is one field that is particularly well-suited for ABM because it involves many individuals that interact in different ways. ABM is capable of explicitly modeling these individuals and the interactions among them, which facilitates the discovery of system behavior that cannot be observed using other methods. ABM has been applied successfully to several focus areas within the field of healthcare operations management, including, but not limited to: healthcare delivery, epidemiology, economics, and policy. In this chapter, we review and evaluate a selected body of research in which agent-based modeling and simulation techniques are applied to problems in healthcare. We also highlight specific areas where agent-based modeling and simulation filled a significant gap that was not addressed previously by other methods. Finally, we propose some new questions in the field which may be of interest moving forward.

Optimization methods have been applied to a wide variety of problems in healthcare ranging from operational level scheduling decisions to the design of national healthcare policies. In this chapter, we provide an overview of several practical optimization applications in the domain of healthcare operations management, including appointment scheduling, operating room scheduling, capacity planning, workforce scheduling, healthcare facility location, organ allocation and transplantation, disease screening, and vaccine design. We provide detailed examples to illustrate the use of different optimization techniques such as discrete convex analysis, stochastic programming, and approximate dynamic programming in these areas.

Operating room (OR) planning and scheduling decisions involve the coordination of patients, medical staff, and hospital facilities. The patients arriving to the hospital are assigned to a surgery date and a surgery time slot. At the time of surgery, a suitable OR, the attending surgeon, supporting anesthesiologists, nurses, and, after the surgery, rooms in secondary facilities such as post-anesthesia care unit (PACU), intensive care unit (ICU), and ward need to be available. In order to deal with the complexity and the variety of problems faced in OR scheduling, it is useful to involve methods from operations research. In this chapter, we review the recent literature on the application of operations research to OR planning and scheduling. We start by discussing the impact of planning and scheduling of the ORs on the overall performance of a hospital. Next, we discuss the criteria for included publications and summarize the structure of Cardoen et al. (Eur J Oper Res 201:921–932, 2010) that served as the guideline for organization of this chapter. In the remainder of the chapter, we describe the evolution of the literature over the last 10 years with regard to the patient type, the different performance measures, the decision that has to be made, the incorporation of uncertainty, the operations research methodology, and the applicability of the research. Moreover, each of these evolutions will be demonstrated with a short review of some relevant papers. This chapter ends with conclusions and a discussion of interesting topics for further research.

Intensive care units (ICUs) are limited-capacity, resource-intensive wards in a hospital designed to provide continuously monitored, intensive care and temporary support to critically-ill patients with a broad range of health conditions. Therefore, their efficient operation and management are critical to providing quality care to the most severely ill patients and to reducing costs for healthcare providers. Computer-based simulation and analytical models have historically been used to analyze ICU operational outcomes such as patient waiting times, bed occupancy rates, denied admission rates, and daily operating costs. This chapter highlights the variety of models and techniques that are prevalent in the modeling, analysis, and management of ICUs. Additionally, we describe an ongoing, multidisciplinary research project whose aim is to develop an empirically-validated discrete-event simulation model to analyze the performance of multiple ICUs at a local Veterans Affairs (VA) hospital.

Healthcare organizations face a challenging operational environment characterized by uncertain demand, the need to deliver highly complex and specialized services, and increasing pressure to provide better quality care to more patients at lower total cost. Healthcare organizations invest substantial financial resources into the human, technological, and structural assets needed to provide a wide range of healthcare services. Determining how much capacity is made available, how that capacity is allocated to patients, and how various specialized units in the organization coordinate their activities are important drivers of performance. In particular, healthcare organizations continuously evaluate the flow of patients through the organization as different healthcare services are provided. If patients do not flow smoothly through the healthcare delivery process, either due to inadequate capacity or the inefficient use of capacity, then patient satisfaction and quality of care can suffer. This chapter provides an overview of the concept of patient flow as one measure of the quality and effectiveness of healthcare delivery, examines some of the most significant challenges to improving patient flow, provides an overview of prior operations research related to patient flow, and discusses current factors that are driving future research opportunities.

We discuss capacity allocation for primary care practices at three different planning levels: the strategic, the tactical and the operational. The goal in each case is to maximize two important but often conflicting metrics: (1) timely access and (2) patient-physician continuity. Timely access focuses on the ability of a patient to get access to a physician as soon as possible. Patient-physician continuity refers to building a strong relationship between a patient and a specific physician by maximizing patient visits to that physician. Each primary care provider (PCP) has a panel of patients for whose long term holistic care the PCP is responsible. At the highest or strategic level, the design of physician panels, we demonstrate the impact of case-mix, or the type of patients in a physician’s panel, and show how panels can be redesigned effectively. Panel redesign, however, involves changing existing patient-physician relationships. A viable alternative is managing the inherent flexibility of PCPs to see patients of other physicians. At the tactical level, this requires allocating the flexible capacity to two types of appointments: 1) prescheduled appointments which are booked in advance and require continuity; and 2) same-day appointments. Using a 2-stage stochastic optimization model, we show that greedy algorithms find the optimal capacity allocation, and find that a partially flexible practice provides a good compromise between timely-access and continuity. Finally, at the operational level, the implementation of flexibility during a workday has to be made under partial demand information, as patient calls arrive over the course of a day. We discuss the impact of flexibility and suggest heuristics that practices can use in this dynamic case.

This chapter examines the barriers to, and opportunities for, improving the planning and scheduling of complex outpatient care environments. First, this chapter explores the effects of two phenomena—complexity and uncertainty—that exacerbate operational challenges for clinics. Mitigating the effects of these phenomena is motivation for proposing a framework for managing clinic operations more effectively. By collecting historical data, and using it effectively, staff are better able to create realistic plans for upcoming clinics. The chapter reviews some of the analytical techniques that can be used to then improve that plan, including an example of a mixed-integer program for determining the optimal sequence of clinical activities. Once the clinic session has begun, the nature of the decisions being made in real time is more tactical, but similar analytical approaches may be useful. Combined with powerful information technology, a proposed analytical and coordination framework can help guide systems improvement efforts. The chapter then concludes with discussion of several opportunities for future research on methods for improving decision-making in clinical operations management.

Patient no-show is a pervasive problem in outpatient clinics. This chapter provides a literature review and discussion on how to develop statistical no-show models. The literature review is a structured and representative selection of research studies from a variety of medical areas. The literature is grouped into four classes. The first class covers self-reported reasons for no-show. The most common self-reported reasons are forgetting, conflicts, transportation, scheduling system problems, and physical or mental illness. The second class discusses the effect of no-show interventions such as appointment reminders, patient education, and changes in scheduling systems on no-show behavior. The third class develops statistical models of no-show behavior in a variety of settings. Several patient, provider, and clinic characteristics are considered in developing these models. The last class of literature considers the impact of no-shows on health outcomes, which illustrates the importance of no-show modeling. The second part of the chapter explains how statistical no-show models can be developed. The data requirements, determination of significant factors, development of logistic regression models, and model validation are explained in detail. An example no-show model is provided to illustrate the modeling and validation process. The chapter concludes with summarizing thoughts and a discussion of future research opportunities.

In this chapter we discuss operations research models and methods for simulating and optimizing ambulance operations. We also discuss our experiences in developing and applying software that implements these techniques. We describe a new simulation-optimization algorithm for base location. We also present a case study detailing how the software we developed was used as part of a major reorganisation of ambulance operations in Copenhagen, Denmark. This chapter also examines the complex problem of real-time ambulance relocation. We review the literature in this area, and describe a new real-time ambulance re-positioning optimisation model and associated software now being used by ambulance operators in several countries to improve their operations.

Along with their federal, tribal, and state counterparts, local health departments, as part of the public health system, are on the front lines to assist the community to prepare, respond, and recover from public health emergencies. Local health departments have many roles during a public health emergency; one important role is to create a successful mass prophylaxis operation for the public using points of dispensing (PODs). This chapter outlines the specific functions necessary for a successful and efficient POD operation. A step-by-step process is described and can be used both for emergency and everyday planning of POD operations. In addition, researchers continue to work to solve many of the complex challenges facing local health departments attempting to prevent morbidity and mortality during a biological disaster. A discussion of some of these challenges and opportunities for future research is outlined in this chapter.

In this chapter we focus on the detailed, operational modeling of Emergency Departments—the flows and general processes, and not on clinical decision making. On the flow level, an Emergency Department shares a number of characteristics with a general repair shop, and while there are key differences, the flow and resource interrelationships are similar. We use this perspective to assist researchers with the decomposition and analysis of Emergency Departments, as well as the review of detailed research on Emergency Departments. We examine the scope of research efforts, methodologies employed, types of data included in the modeling, and the implementation of research results in practice. The chapter is anchored by an extensive field study at a medium-sized Emergency Department, whose methodology and key results are presented along with insights from the hospital.

Determining the location of health care resources plays an integral role in societal planning of health care delivery. Ensuring that populations have proper access to health care resources such as preventive care, treatment facilities, and emergency services is often the primary criterion decision makers use in locating such resources. Location decisions in health care settings present unique challenges that need to be considered such as the implications on a population’s health outcomes. In this chapter, a diverse range of health care applications of location models are discussed. Common location model formulations are presented along with discussions of when each model formulation may be preferred. A case study is presented based on the location of stockpiled antidotes for a nerve agent response in North Carolina. Solutions to the location model formulations discussed are compared along with a solution resulting from an easy-to-implement heuristic. Future opportunities for location models in health care settings are identified.

Rising costs and increasing patient expectations have heightened the need for better coordination of health services. In this chapter we discuss the role that operations research methods such as system dynamics and discrete-event simulation can play in pursuit of such coordination. Most healthcare services are operated independently from others, although patient treatment often requires visiting several services. Also, most operations management methods have focused on improvement or design of individual services. We will highlight some of the research that considers coordination among multiple services, and present two case studies that address coordination at the healthcare system and hospital levels. We also discuss some new trends in healthcare services delivery that have significant implications for operations researchers.

Hospital operations and patient care are inextricably linked to the supply chain. Like buyers in other industries, pharmacy material managers are challenged to develop inventory policies in light of changing demand, limited suppliers, manufacturing issues, and regulatory rulings that affect drug supply. While these challenges are similar to those in many industries, the impact of drug shortages is quite different as they can have detrimental impacts to patient care and the cost of care. This chapter addresses the inventory management of supplies that directly impact patient care with a focus on pharmaceuticals. The chapter discusses the unique modeling challenges associated with inventory decisions for these products such as their multi-echelon nature, the potential for perishability and/or obsolescence, and internal production/preparation lead times. The chapter also explores the broader implications of the impact of demand uncertainty and inventory management strategies on hospital operations and cost with a particular focus on the comparison between stationary and nonstationary demand. Finally, future research opportunities are discussed.

The practice of patient care is supported by a range of healthcare supply chain management activities, also referred to by many as healthcare logistics. Improving the efficiency of these activities can provide opportunities for healthcare institutions and health systems to increase the quality of care and reduce costs. Hospitals represent a key link in the supply chain and face their own particular challenges due the complexity of their internal supply chain. The distribution of medical supplies to nursing units represents a vital component of the internal hospital supply chain. Indeed, all doctors, nurses, and clinical support staff deal with and depend on supplies in one way or another and are thus affected by their accessibility and availability on a daily basis. The methods most often used to distribute supplies to nursing units range from requisition-based systems that depend on clinical staff involvement to methods where the hospital’s central stores manage inventory and replenishment. In the latter category, common storage and distribution method options include par level systems and automated cabinets, among others, as well as the two-bin/kanban replenishment method, which has been identified as a best practice. For its part, RFID technology has further enhanced this leading practice and introduced the possibility of proactively managing supplies by triggering replenishment rounds based on a range of user-defined criteria. Beyond its expanded benefits, this innovation opens the door to a large number of research avenues in the areas of capacitated vehicle routing problems, inventory optimization, and simulation.

Healthcare access in sub-Saharan Africa is acutely inadequate. The last mile of the health delivery supply chain, in particular, is a critical bottleneck to healthcare access in this and other regions. There are a number of challenges to building relevant models for resource-limited settings, including a lack of data and performance indicators. Nonetheless, operations research methodology can be used to evaluate and improve the last mile of health delivery. We present a case study of our work with Riders for Health, a nonprofit organization that addresses the problem of last-mile distribution by providing transportation solutions for health workers in Africa. In particular, Riders uses driver training programs, routine maintenance, and efficient spare parts management to improve vehicle uptime. The inventory and queuing model that we have developed of Riders’ fleet management program was informed by numerous visits and interviews conducted over the past two years, as we set up a trial to evaluate Riders’ effectiveness. With our model, we identify Riders’ most effective initiatives as well as advise the organization on its priorities.

Decisions regarding the best use of scarce health resources become increasingly complex in developing countries due to high disease incidence, poor healthcare system infrastructure, and other societal factors. We develop a resource allocation model for the design of an Indoor Residual Spraying (IRS) program for malaria prevention in developing countries. Due to the seasonal nature of malaria risk factors, the model addresses dynamic resource allocation based on the risk characteristics. Using the model as a framework, a decision support tool for IRS operations is constructed. With a small numerical example we demonstrate the value of the tool for evaluating complexities and tradeoffs in the allocation of limited resources for an IRS program and the impact of heuristic decision making.