Keywords

1 Introduction

The goal of researchers working in healthcare management is to control the rising costs and to increase accessibility for healthcare services. They try to do this by integrating the aspects of management with Operations Research (OR) techniques to determine the most efficient (or optimal) methods of providing patient care delivery (Langabeer 2007). The studies of OR in healthcare are not only about determining the methods for healthcare delivery, but also about simulating clinical systems to observe long-term risks.

Operations Research reached the stage of maturity in a very short time after it was first applied during WWII (Kirby 2003). It has been considered a discipline hard to grasp even though it spread to a wide application area. OR techniques are used to model and solve real-world problems in different areas such as production, logistics, etc. (Hillier and Lieberman 2005). Healthcare is yet another area, a relatively new one that Operations Research techniques are used in.

Healthcare is a business-like no other. Carter (2002) pointed out that it has multiple decision-makers with conflicting goals and objectives. Moreover, healthcare business has a high level of uncertainty as well as dynamic relationships among its components. Also, the managers in healthcare demand to lower the costs and increase the service quality. These aspects render management of healthcare and its operations reasonable to be studied with OR techniques.

The objectives of OR studies in Healthcare Management are to control the costs and to improve the quality of healthcare services (Mclaughlin and Hays 2008). For the last two decades, hundreds of articles were published, special journal issues were put together, and conferences were organized. Various studies are carried out in different areas of healthcare. Some of these areas are resource allocation, scheduling, managing waiting lists, streamlining patient flows, facility location, cost-effectiveness analysis, triage in emergency services and disease treatment investigations (Pierskalla and Brailer 1994).

Although a comprehensive taxonomic classification was made by Hulshof et al. (2012), an up-to-date taxonomy is still a necessity. This chapter aims to provide a general overview of OR studies in healthcare management, using a different taxonomy approach than that of Hulshof et al. The literature has been thoroughly reviewed, and by classifying previous studies according to their preferences, a taxonomy for OR studies in healthcare management has been prepared.

2 OR Studies in Healthcare Management

Healthcare management research was first established in the 1930s. Although there had been some studies before, application of operations research in healthcare is accepted to have started during the 1970s. First publications were mainly about health planning and administration (Stimson and Stimson 1972; Shuman et al. 1975; Fries 1981). Later on, research areas on healthcare widely spread from top management to the smallest operation.

OR studies in healthcare management became popular during the 1990s. Using OR techniques in healthcare attracted a lot of attention in many countries and lots of studies are currently carried out (Luss and Rosenwein 1997). Many universities and research groups have shown interest in the subject. For example, McGill School of Environment (MSE) has a program called “Healthcare Operations & Information Management,” directed by Vedat Verter. Although research is generally centered in the USA and Canada, a working group of Association of European Operational Research Societies (EURO) called Operational Research Applied to Health Services (ORAHS), which was initiated in 1975, provides a network for researchers involved in the application of systematic and quantitative analysis in support of planning and management in the health services sector.

There are already some bibliographic studies that organize the papers and classify them (Flagle 1962) classified the problems encountered in the area. Fries (1976) organized the papers before 1975; and the literature between 1970 and 1989 was classified by Corner and Kirkwood (1991). Also, minor classifications were made in the following years. Preater prepared a bibliography on the application of queuing theory in healthcare and medicine (2002); Cayirli and Veral reviewed the literature of outpatient scheduling in healthcare (2003); Lowery (1996) and Jun et al. (1999) investigated the simulation applications in health services. Due to these bibliographic studies, this study excludes papers published before 2000, and focuses on more recent years where the literature is building up more quickly than ever.

The following keywords were used to find research papers published in the literature: healthcare operations management, healthcare management, health services, healthcare applications, health workforce planning, ambulance allocation, hospital resource allocation, outpatient scheduling, nurse–patient assignment, healthcare delivery, doctor/nurse workload, operating room planning, healthcare operations, doctor/nurse scheduling, health care production, emergency patient flow, health care services, and management decision support in the health service. The results of the search yielded over 500 articles, mainly in these journals: Annals of Operations Research, Artificial Intelligence in Medicine, Computers & Operations Research, European Journal of Operational Research, Expert Systems with Applications, Health Care Management Science, Health Policy, IIE Transactions, Interfaces, Omega, Social Science & Medicine, and Socio-Economic Planning Sciences. Before proceeding to the taxonomy, some exclusion criteria were determined in order to narrow the findings. Thus, we ended up with articles that were more related to the subject of “application of OR to healthcare management”. These exclusion criteria were studies not in English, studies without models (Review papers), studies about improving treatment and diagnosis (screening, analyzing outputs, etc.), models based on probability and statistics, and models based on economic theory. As a result, 142 articles were within the criteria. Their distribution by the year of publication may be seen in Fig. 1.1.

Fig. 1.1
figure 1

Number of publications by years

Full size image

3 Necessity for a Taxonomy: A Discussion

The size and growth rate of the literature demands a systematic way to classify various contributions in a manner that will vividly provide a panoramic view of what exists and will also clearly identify any existing gaps as suggested by Reisman (1992) and Reisman (1993).

Taxonomy may be defined as the science of identifying objects, and arranging them in a classification. According to Gattoufi et al. (2009), it is not only an efficient and effective tool for systematic storage as well as a tool for teaching/learning, and a tool for recalling knowledge, but it is also a neat way of pointing to knowledge expansion. It identifies voids, potential increments (or developments) in theory, and potential applications involving the existing theory. The basic motivations and uses for taxonomy may be listed as follows (Eksioglu et al. 2009):

  • It defines or delimits the boundaries of a subject domain, and that is, in itself, useful information.

  • It vividly, efficiently and effectively displays all of that domain’s attributes/dimensions.

  • It vividly, efficiently and effectively displays that any one of the possible combinations of these attributes/dimensions defines or delimits the boundaries of a subject sub-domain.

  • It allows one to have a panoramic view of the entire “forest” while examining and classifying a given “tree.”

  • It allows one to unify disjointed and disparate subfields or sub-disciplines into a meaningful whole.

  • It allows one to organize one’s knowledge about the domain, and this has major implications for teaching, learning, storing, and recalling information.

  • It allows one to identify voids and well explored territories in the extant literature base, which is very important for researchers, funding agencies, and other decision makers.

What is presented here is open for incremental evolution, as is the case in one of the greatest and best-known taxonomies of all time: The Periodic Table of Elements. The classification developed in this study is open to expansion when the scope of OR studies in healthcare management is enhanced, since any taxonomy is delimited with the boundaries of the universe it classifies.

OR studies in healthcare management have already generated a large enough literature to allow it to be considered as a separate and distinct field of knowledge. The increasing interest in the OR studies in healthcare management makes a systematic elaboration of this field more crucial to helping current researchers as well as attracting potential newcomers to the field.

Defining a taxonomy for OR Studies in Healthcare Management may seem to be overly detailed in terms of branching levels, as a result of trying to cover all literature in every subarea of healthcare management research. Although this detailed branching results in a taxonomy that is hard to work with, it increases its descriptive powers. Furthermore, it gives researchers the ability to aggregate sub-classifications and/or pruning outer branches easily. The taxonomy proceeds in a way illustrated by Reisman (1992), which can be seen in Fig. 1.2.

Fig. 1.2
figure 2

Attribute vector description based taxonomy

Full size image

4 A Taxonomy for OR Studies in Healthcare Management

In this section, the taxonomy for OR studies in Healthcare Management (HCM) is presented, and the main features that were considered while building it are introduced. We provide definitions as well as justifications for those main features and provide identification codes for some terms within the context of the taxonomy.

The full taxonomy is illustrated in Fig. 1.3. In the proposed taxonomy, each contribution can be given an identification code based on domains grouped in five classes:

  • Class 1: Study Specifications. This class shows how the study is specified. This is subdivided into three domains. The first domain describes the type of study; the second describes the source of the data used; and the third describes the type of problem treatment.

  • Class 2: Subject. This class shows what is analyzed. Each research paper analyzes one or more subjects. There are seven main subjects and the rest is grouped as “other”.

  • Class 3: Methodology. This class shows the methodology used in the research. Each research paper consists of one or more methods. There are eleven main methods; and the rest is grouped as “other”.

  • Class 4: Problem Specifications. This class shows who and what the problem is analyzed for. This is subdivided into three domains. The first domain describes the people affected by the problem; the second describes the area that the problem occurred in; and the third describes the affected facility by the problem.

  • Class 5: Location Specifications. This class shows where the research was carried out. The model constructed or the problem analyzed can be applied to large, medium or small scale; or it can be non-location-specific.

Fig. 1.3
figure 3

A taxonomy of OR studies in healthcare management

Full size image

5 Results of the Taxonomy with Selected Articles

In this section, by using a group of articles which represent rather different approaches and which address different issues of OR studies in HCM, the taxonomy of Fig. 1.3 is tested for its robustness and its ability to discriminate in a rigorous manner.

One hundred forty-two articles were investigated in detail to see the general idea of the researchers that contributed to the OR-in- HCM literature. In the first class, there are three domains; type of study, data used, and problem treatment. In the type of study domain, the most observed attribute is “model construction using a modified method” or “integration of methods” (1.1.2), followed by “model construction using an existing method” (1.1.1). “Comparison of methods” (1.1.3) is slightly less frequent than these two, since there are not enough studies to make a clear comparison. In data used, “usage of both real data and synthetic data” (1.2.2) is more frequent than “just using real data” (1.2.1), which can be explained by the difficulty of collecting real data as well as the highly popular usage of simulation that easily creates loads of synthetic data. For treating the problem, it is mostly “decision making” (1.3.2) rather than “situation analysis” (1.3.1). So, speaking for the study specifications, the papers mostly consisted of “decision making” with the “usage of both real and synthetic data” by “constructing a model using a modified method”.

The second and third classes are the classes that give researchers direction. It is better to interpret these two classes by looking at them together. Thus, the researcher may be able to pick the method to use for the subject he/she works on. However, first, one would need to check where previous studies have focused on. In the second class, “patient flow” and “waiting list management” (2.6) is the most researched subject. This is followed by “scheduling and assignment” (2.4), and “performance measurement” (2.2). “Resource/budget allocation” (2.5), “planning and design” (2.1), and “capacity management” (2.3) are more generalized subjects, for which researchers need to consider more factors, which means they are harder to model. Thus, they are not as attractive as the first three subjects. “Location” (2.7), and “other” (2.8) subjects have found fewer study areas than the rest, but these studies have been done mostly in recent years, which can be considered new research areas introduced to the discipline.

In class three, where methods are compared, “simulation” (3.3) is the most common method used to model in healthcare management, both alone or integrated with other methods. Simulation is mostly used to model “planning and design” (2.1), “scheduling and assignment” (2.4) and “patient flow and waiting list management” (2.6). Mathematical programming models such as “linear/integer programming” (3.1) and “multi-objective programming” (3.2) are also frequently used in order to model “scheduling and assignment” (2.4), “resource/budget allocation”, (2.5) and “location” (2.7). Following common methods are “data envelopment analysis” (3.4), which is used mostly for “performance measurement” (2.2) and “stochastic methods” (3.7), mostly to model “capacity management” (2.3) problems. “Multi attribute decision making” (3.8), “game theory” (3.9) and “artificial neural networks” (3.11) are the least used methods as they have been introduced to healthcare operations management area in recent years.

Fourth class is where the problem details are explained. It includes three domains. In the first domain, concerning people, the most affected and investigated group in the papers is “management “(4.1.1). It is followed by “patients” (4.1.4), affected mostly in modeling “patient flow and waiting list management” (2.6) problems. “Doctor/physician” (4.1.2) and “nurse or non-medical staff” (4.1.3) groups are included generally in “scheduling and assignment” (2.4) problems. The second domain seeks whether the problem occurred inside or outside the hospital. Most of them are “hospital/clinic” (4.2.1) problems; the rest is “non-hospital” organizations (4.2.2) or “public health” (4.2.3). Concerning facility is the third domain in this class. Most of the studies include “the entire facility” (4.3.1). “Emergency room” (4.3.2) and “operating room” (4.3.3) are also important research areas for operations research methodology, especially for “linear/integer programming” (3.1) and “queuing theory” (3.5). “Ambulances” (4.3.4) and “nursing homes” (4.3.5) are the facilities that have been gaining importance in recent years.

The last class is the location where the research in a given paper is carried out. This resulted in “specific location based” (5.3.2) to have the highest frequency. “Country based” (5.2.1) is the second one, because researchers do research under the regulations of specific countries. “Worldwide” (5.1.1) and “continent based” (5.1.2) are the lowest location types that appear in these papers, since it is hard to construct a model that can be applied to a very large scale in a world with so many varieties.

6 Conclusions and Further Suggestions

Selection of papers for any taxonomy is a subjective work. The taxonomy described in this chapter tries to represent a variety of studies with different journals, different authors from different countries, differing paths to theory extension, differing application sectors and differing research strategies.

Being a new Management Science sub-discipline, the OR literature in healthcare management is growing exponentially like the other new sub-disciplines. This literature is recording advancements in theory and in solution methodology while at the same time expanding its domain of applications. When the previous bibliographic studies are compared with this taxonomy, it can be seen that new research areas are added to the discipline; new methods are used to model problems, and new approaches are applied to improve outputs. In spite of all these developments, research subjects are still divided as Fries (1976) stated; and simulation is still the most commonly utilized method to model problems as Jun et al. (1999) mentioned.

This taxonomy is formed with the motivation to determine application areas and specifications of OR studies in healthcare management as guidelines for future avenues of research. For future research, the most focused areas can be determined and the deficiencies in those areas can be satisfied with different approaches. For example, performance measurement problems are usually modeled with data envelopment analysis and from the management point of view. Therefore, using different methods or looking from a different angle, management can help eliminate the drawbacks of the previous studies. Or the least focused areas can be chosen to work on, such as performance measurement of the emergency room, which has not yet been studied. Also, with the addition of new areas as a result of the growing literature, improvements on this taxonomy can be made.