SAVING LIVES THE CHEAPEST WAY

HOW THE INFLUENCE OF UNPREDICTABLE DEMAND OF EMERGENCY CALLS, ON RESPONSE-TIME OF AMBULANCES, SHOULD BE REDUCED IN TERMS OF

OPERATIONS MANAGEMENT.

Thesis Organization & Strategy

Academic Year 2010

Author: B.J. Verhoeven

ANR: 712209

Supervisor: Dr. M.A.H. Groen

Words: 7.475

MANAGEMENT SUMMARY

This thesis copes with the unpredictability of demand for an ambulance network. The Dutch government has set a performance target for the ambulance network of fifteen minutes for the urgent calls. This means that the process from calling 112 until arriving at the patient should maximally take 15 minutes. Since the number of urgent calls is hard to forecast, ways to deal with the unpredictability should help to meet these targets. In order to provide new insights operations management solutions to cope with unpredictable demand are combined with the ambulance model. This provides the following problem statement.

PROBLEM STATEMENT:

How should an ambulance network cope with unpredictable demand, in order to meet the required performance with minimum costs?

This problem statement is investigated according to a structured path, which is described with research questions.

RESEARCH QUESTIONS:

- How does operations management deal with unpredictable demand? - Which factors influence the performance and costs of an ambulance network? - How should the solutions, used in operations management to deal with unpredictable

demand, be applied to ambulance networks?

CONCLUSIONS

There are two methods in operations management to deal with unpredictable demand that can be applied to an ambulance network. At first: “Holding finished-goods inventory is a solution to make sure that unpredictable demand can be met.”

Second: “A flexible process design is a solution to be able to produce more or less in order to fulfill unpredictable demand.”

The performance of an ambulance network is influenced by a number of factors. The number and location of ambulances with qualified personnel and equipment is the most important factor. The performance is also influenced by factors that are harder to control. The geographical factors, infrastructure and technology system are other important factors.

When the two solutions from operations management are combined with the aspects of an ambulance model, two possibilities to cope with the unpredictable demand appear. At first increasing the number of ambulances with equipment and qualified personnel would make it possible to handle a high number of emergency calls. This is an expensive solution so a balance between the costs and the need should be made. A decision should be made when an increase in costs outweigh the chance of a life being saved.

The second solution is to create a more flexible process. The current model in the Netherlands is build on the queuing theory, which defines an ambulance as a static service mechanisms. This while ambulances are dynamic. A model could be defined where an ambulance does not dispatch from a fixed basis, but adapts to the number and location of prepared ambulances. When an ambulance responds to a call, another ambulance should move in the first ambulance his direction to assure maximal preparedness. This flexibility makes it possible to cope with unpredictable demand with a minimum of needed ambulances with equipment and qualified personnel.

TABLE OF CONTENTS

1. Introduction ..................................................................................................................................................................... 1

1.1 Problem indication ................................................................................................................................................ 1

1.2 Problem statement and research questions ............................................................................................... 1

1.3 Research methods and structure .................................................................................................................... 1

1.4 Academic and managerial relevance ............................................................................................................. 2

List of figures................................................................................................................................................................... 2

2. Operations management and unpredictable demand ................................................................................... 3

2.1 Inventory ................................................................................................................................................................... 3

2.2 Utilization .................................................................................................................................................................. 4

2.3 Outsourcing .............................................................................................................................................................. 4

2.4 Perfect solution ....................................................................................................................................................... 5

2.5 Conclusion ................................................................................................................................................................ 5

3. Ambulance networks ................................................................................................................................................... 6

3.1 The technology system ........................................................................................................................................ 6

3.2 The ambulance process ....................................................................................................................................... 6

3.3 Geographical aspects and infrastructure ..................................................................................................... 8

3.4 Other variables ....................................................................................................................................................... 9

3.5 Conclusion ................................................................................................................................................................ 9

4. Ambulance networks and unpredictable demand ....................................................................................... 10

4.1 Proposition 1 ........................................................................................................................................................ 10

4.2 proposition 2 ........................................................................................................................................................ 10

4.3 Conclusion ............................................................................................................................................................. 12

5. Conclusions and discussion ................................................................................................................................... 13

5.1 Conlusions.............................................................................................................................................................. 13

5.2 Discussion .............................................................................................................................................................. 13

5.3 Recommendations .............................................................................................................................................. 14

6. References and other sources ............................................................................................................................... 15

6.1 References ............................................................................................................................................................. 15

6.1 Other sources ....................................................................................................................................................... 16

7. Appendix: E-mail from the RIVM ......................................................................................................................... 17

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1. INTRODUCTION

1.1 PROBLEM INDICATION

For emergency calls in the Netherlands a maximum response time of 15 minutes is required for every location. A trustworthy logistic network is needed to meet this requirement. Assuming that an ambulance can reach a maximum mean speed of 120 kilometers per hour, which is rather high for a mean speed, an ambulance must be available within 20 kilometers of every location to meet the requirement. Especially when obstacles like traffic jams are taken into account these 20 kilometers will be even less.

Clearly a lot of variables influence the performance of an ambulance network, which is measured in the percentage of calls that is handled within 15 minutes. Since the effects of a fast response time on chance of survival are proven to be significant (Bevan & Hamblin, 2009), good performance is a real life saver. The fact that there are a lot of different systems to cope with ambulance networks already states that there is not one perfect model. The biggest cause of these varying models is the unpredictable demand. Research (Dalton, Holmes, & Slifkin, 2003) shows that there is a large instability in demand, especially in places where few people live. The challenge is to cope with this unpredictable demand.

1.2 PROBLEM STATEMENT AND RESEARCH QUESTIONS

The easiest solution would be to put an ambulance on every corner of a street. However this is not a possible solution since paramedic units are extremely costly (Mayer, 1980). Therefore there is a tension between performance and costs. This tension is increased by the unpredictable demand, which threatens the performance of an ambulance network. Therefore the following problem statement is formulated.

Problem statement:

How should an ambulance network cope with unpredictable demand, in order to meet the required performance with minimum costs?

Research Questions:

How does operations management deal with unpredictable demand?

Which factors influence the performance and costs of an ambulance network?

How should the solutions, used in operations management to deal with unpredictable demand, be applied to ambulance networks?

1.3 RESEARCH METHODS AND STRUCTURE

This research is based on a literature study. It combines the literature of operations management with researches of ambulance networks in order to find new insights in modeling ambulance networks. The chapters 2 till 4 deal with the defined research questions and chapter 5 contains the conclusions of this research. This thesis copes with the ambulance network in the Netherlands. Since this model is set up with a method that is comparable to the method other countries use, information of other countries is used and applied to the situation of the Netherlands.

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The information on which this research is based is gathered in two ways. First a search in the library has provided information about unpredictable demand in operations management. Second a search in the internet has provided information about ambulance networks. Especially “Jstor.org” and “web of science” provided a significant list of articles when search terms like “ambulance” and “healthcare” were used.

With the searching of articles the references of the article were used for further searches. For instance an article that provides a framework is based on several other studies that can be interesting too. This method provided a long list of articles. From this list the most important articles are filtered. This is not only done by grading the essence of the information, but also by grading the credibility of the source that printed the article. Therefore important sources like:

“Management science” impact factor: 2,354

“Operations Research” impact factor: 1,463

“Journal of the operational society” impact factor: 0,839

are the basis of this investigation, which makes the investigation credible. A lot of articles were found, but not all articles were relevant. Some of them did not contain useful information, others were left out of the investigation because of doubts about their credibility. This credibility is based on the earlier named impact factors, which are based on the number of times an article is cited. The more times an article is cited in other article, the more credible it appears to be.

Information about the ambulance system in the Netherlands was hard to find, so an email was sent to the RIVM to get insight in the Dutch ambulance model. This email is published in the appendix.

1.4 ACADEMIC AND MANAGERIAL RELEVANCE

Every day people die because the ambulance was not in time to save them. This is not only frustrating for the family of victims but also for the ambulance personnel. Therefore it is relevant to investigate the options to cope with unpredictable demand.

The academic relevance appears from the investigations that already exist. There is a lot of literature on modeling ambulance networks with for instance queuing theory. This investigation will compare investigations and provide new insights, which should make it easier to define new models.

LIST OF FIGURES

FIGURE 1: AMBULANCE PROCESS (FITZSIMMONS, 1973) PAGE 7

FIGURE 2: PERCENT OF AMBULANCE CALLS BY ZONE (SWOVELAND,

UYENO, VERTINSKY, & VICKSON, 1973) PAGE 8

FIGURE 3: THE BASIC QUEUEING PROCES (HILLIER & LIEBERMANN, 2005) PAGE 11

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2. OPERATIONS MANAGEMENT AND UNPREDICTABLE DEMAND

In this chapter the ways of handling unpredictable demand in operations management are analyzed. This is done from the view of a production process. It may seem to make more sense to analyze unpredictable demand in a service network, since an ambulance network can easily be compared to a service network. However research shows that ambulance networks are set up with the queuing theory (Taylor & Templeton, 1980) (Swoveland, Uyeno, Vertinsky, & Vickson, 1973) (Baker, Clayton, & Taylor, 1989), which is also a the most common used theory for service modeling (Gaither & Frazier, 2002). This theory might exclude some possibilities of dealing with unpredictable demand, since it the queuing theory is based on fixed aspects with do not totally match with an ambulance network. Therefore a production view may show other insights that can be translated and applied to ambulance networks.

Planning and control is the basis for a good process design (Gaither & Frazier, 2002). This statement directly implies the problem of unpredictable demand. When the demand for a product is not known in advance, it is hard to plan activities to meet demand. This problem has three possible outcomes. At first there is the small possibility that demand matches the capacity of the process design, in which case a company can fulfill demand against the lowest possible costs. There is however a significant larger chance that the demand is lower or bigger than the capacity of the process design. In case the demand is lower than the capacity there is unused capital, what drives the costs. The possibility exists that the capital costs are higher than the revenues of demand, which means loss. In case the demand is higher than the capacity, not enough products can be made to fulfill demand, which means “selling no” to customers. “Selling no” is one of the biggest threats for a company’s reputation and indirectly leads to losing revenues (Hillier & Liebermann, 2005).

Lost sales are seen as the worst threat and because of this threat it is important that a company is flexible enough to adapt to varying demand. Gaither and Frazier (2002) define two possibilities to prevent the effect of “selling no” from happening without a need to increase capacity. The first possibility is to use safety stock. When inventory is high enough to meet the largest possible demand, a company never has to “sell no”. The second possibility is to increase utilization. There are several options to increase utilization and it depends on the process which option fits best.

2.1 INVENTORY

The main reason to keep finished-goods inventory is made pretty clear, but there are also reasons why companies do not want this inventory. The biggest reason is the carrying costs that large inventories have. These are costs to insure, finance, store, handle and manage inventories.

In the case of independent demand the level of inventory is based on forecasts (Gaither & Frazier, 2002). The mean variability of these forecasts determines how much safety stock is needed. In case of unpredictable demand the variability is high so there is also a large inventory of finished-goods needed. Therefore the second option may be more attractive form a costing view.

Proposition 1: Holding finished-goods inventory is a solution to make sure that unpredictable demand can be met.

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2.2 UTILIZATION

Utilization means using the capacity as intensively as possible. Every machine or person that stands still is seen as waste (Gaither & Frazier, 2002). A process design with maximum utilization produces with minimum capital costs. In practice however it is almost impossible to reach a 100% utilization level. This is caused by factors as downtime, changeover time etcetera. The challenge for process designers is therefore to get utilization level as high as possible.

There are many options to increase utilization (Slack, Chambers, & Johnston, 2009). For instance the downtime should be reduced as much as possible. This can be done with special forces of technicians that repair machines in short time or just better machines. Further production with bigger batch sizes reduces the change over time. These two examples however are not especially useful for situations where demand is unpredictable.

When demand is unpredictable the most important issue to maximize utilization is flexibility. When there is a low demand just a few machines can do the production. When demand gets high there should be more machines to be able to produce enough. For small companies there are not many options to vary the capacity. There are some examples of companies that rent their capital and have a contract that enables them to hire more or less machines every period of time (Gaither & Frazier, 2002). For big companies there are far more options to increase utility. Most of the time a machine can be used for more than one production process. With a good planning and control, it is possible to handle a varying production. When for instance the production of product A decreases but the production of product B increases, the total production hardly varies. Therefore large companies use a multi functional process design that can easy switch from one product to another. A flexible company can have a high utility level, what reduces the need of inventory and therefore reduces costs (Hillier & Liebermann, 2005).

Biggest disadvantage of high utilization is that the higher the level of flexibility, the higher the costs to implement and maintain it. Also there is always a factor of risk when a company depends on the flexibility to keep utilization as high as possible. With maximum utilization it is hard to increase production on short time (Gaither & Frazier, 2002). This is best explained with an example. A company with 100% utilization, produces 24 hours a day and 7 days a week on maximum capacity level. When one of the machines has a defect, there is no room to catch up with the planned production by having people doing overtime. Therefore production will get behind schedule. For this reason most companies try to maintain a utilization level of 80% (Gaither & Frazier, 2002). This may not sound profitable but when the fact that an average company has a utilization level of only 15% is taken into account, 80% is really high. Main reason why an average company has such a low level of utilization is simply that most companies do not have the faintest idea of their utilizations level. They all think they are doing well until they are confronted with their performance (Hillier & Liebermann, 2005).

Proposition 2: A flexible process design is a solution to be able to produce more or less in order to fulfill unpredictable demand.

2.3 OUTSOURCING

A third option to control variable demand is to outsource. Companies should only perform their core business, since that is what they are best at. For instance a production company could outsource production process maintenance to a company that is able to maintain the production process more efficiently and effectively. That way the production company can focus on producing their product. A disadvantage of outsourcing is that the company loses some control

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over quality. (Gaither & Frazier, 2002) Also the costs per unit product may increase. (Gaither & Frazier, 2002).

The problem statement of this thesis was: “How should an ambulance network cope with unpredictable demand, in order to meet the required performance with minimum costs.” Outsourcing does not fit in this statement, since it does not provide options to cope with the unpredictable demand by means of an ambulance network. It only replaces the problem to another organization that has to cope with the same problem statement all over again. Therefore outsourcing is further left out of this investigation.

2.4 PERFECT SOLUTION

The biggest trouble of unpredictable demand is that there is no perfect solution. Both options discussed above help to control the meeting of demand, but both have their problems. High inventory increases the costs and high flexibility is one of the hardest things to achieve and also quite risky. Therefore a mix of both solutions is advised to most companies that suffer with an unpredictable demand. Every company has its own best solution, which in most cases means a level of utilization of 80% and an inventory as low as possible. (Slack, Chambers, & Johnston, 2009)

2.5 CONCLUSION

From an operations management view there are two ways of dealing with unpredictable demand that could be applied to the ambulance models. At first holding finished-goods inventory is a solution to make sure that unpredictable demand can be met. Finished-goods inventory makes it possible to adjust to a demand that outreaches the production capacity. Secondly a flexible process design is a solution to be able to produce more or less in order to fulfill unpredictable demand. Ways of adjusting the production process to the demand can efficiently cope with unpredictable demand.

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3. AMBULANCE NETWORKS

To be able to compare the two defined solutions for controlling production in a situation with unpredictable demand form an operations management view with the ambulance network, it is important to have insight in the variables of an ambulance network that determine the performance and costs of a network. An ambulance has simply said two task; medical treatment of patients and transportation of patients (Andersson & Värbrand, 2007). These two tasks do not seem to be very complicated, but the biggest challenges in healthcare are not treatment or transport but scale, complexity and change (Harper & Pitt, 2004). These are exactly the problems that occur in an ambulance network as will become clear in its description. In the description of an ambulance network it is important to state that an ambulance is seen as an ambulance including certified personnel and equipment.

3.1 THE TECHNOLOGY SYSTEM

There are different technology systems used over the world. These systems can be descriptive or mathematical. The advantage of a mathematical system is that it provides optimal solutions, while a descriptive system has the advantage that it has better user-interfaces. (Repede, Jeffries, & Hubbard, 1993) In the Netherlands the system C2000 is used for receiving and transforming emergency calls. This system is introduced in 2008 and has the big advantage that it does not only connect the over 3500 ambulance workers but also police and firemen. The call to a technology center takes a few steps (Athey & Stern, 2002):

(i) Someone calls the emergency number (ii) The call is answered and evaluated by a call-taker. (iii) The call-taker communicates with service agencies for dispatch of emergency

personnel. (iv) Call-taker can provide additional instructions.

All these steps are very important, even the first one. Research showed that adequate reactions of bystander, not only help but also calling 112 directly, increases the surviving chances significantly (Brodsky & Hakkert, 1985).

3.2 THE AMBULANCE PROCESS

When the call is evaluated by the call-taker it is given one out of two classifications; urgent or not-urgent. This directly influences the system. The most important factor of an ambulance system is the availability in combination with range (Groom, 1977).This availability must cover every corner of the Netherlands. This is explained best by an example.

Say there is one ambulance available to offer help within 15 minutes in section A and there are multiple ambulances available in section B that can offer help in section A within 25 minutes. A call-taker receives a not-urgent call in section A and sends the ambulance of that section to the scene. Two minutes later there is an urgent call in section A and now the call-taker is forced to send an ambulance from section B to the scene, which might not reach in time to save the patient. It would have been best to send an ambulance from section B to the not-urgent call, so availability in section A was still guaranteed.

When the call-taker is contacted for an emergency call the following process is started:

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Dispatch delay is the time between notifying the emergency system and selecting an ambulance to respond and normally takes less than one minute. When the ambulance is contacted they prepare the ambulance and drive to the patient. When the ambulance arrives at the scene the ambulance workers provide first medical treatment and after that the ambulance drives the patient to the hospital. There the patient is transferred and the work of the ambulance is done. The time from the moment the ambulance leaves until the moment the ambulance workers transfer the patient is called the service time (Fitzsimmons, 1973).

This process is seen as a classical multi-server queue model which can be simulated by linear programming, as will be explained in chapter 4. The model may seem quite simple, but the geographical factors and unexpected variables make it quite complicated to develop an ambulance network. Therefore this subject has been researched a lot and research varies for different countries. It seems rather impossible to formulate a perfect model that fits every country and accounts for every possible variable. This is explained by the big differences between countries. The Netherlands is a rather small country with small distances between cities. On the contrary the United States is a large country with sometimes enormous distances between cities. Both countries require a totally different approach in modeling an ambulance network (Baker & Fitzpatrick, 1986).

The process that is ascribed above is the basic operational process. To keep the process operational there are several important aspects that influence the ambulance network process. An ambulance network has to work 24 hours a day and 7 days a week. Therefore the planning of reloading equipment has to be done properly to assure that availability of ambulances does not decrease to disrupt the model. One of the tasks of an ambulance is to provide first medical treatment (Andersson & Värbrand, 2007) and to be able to do this it has to have the right equipment. This equipment to be refilled every number of calls. Since an ambulance has only a limited space to keep inventory inside, it can only take the most necessary equipment to a call. Also material problems and refueling are aspects that are hard to plan. An ambulance that only handles one call on a random day may not need to be refueled, while an ambulance that handles more than one call may need to refuel several times a day. These aspects influence the preparedness of the ambulance network (Andersson & Värbrand, 2007).

In the Netherlands there are over 3500 people working on an ambulance as is stated in a press release of the RIVM. These 3500 people achieve 92% response to urgent calls within 15 minutes. The RIVM has calculated that about 20% of the missed calls (urgent calls that are not met within 15 minutes) is caused by insufficient availability of ambulances. The other 80% is caused by other reasons like a traffic jam. (RIVM, 2003)

FIGURE 1: AMBULANCE PROCESS (FITZSIMMONS, 1973)

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3.3 GEOGRAPHICAL ASPECTS AND INFRASTRUCTURE

The biggest challenge in modeling an ambulance network is forecasting the number of emergencies that will appear in every sector. Dealing with this variety is also the key problem of this research. In theory the highest frequency of calls is in the area where the most people live. The variability of number of calls however appears to differ without any known reason than that it is just coincidence. There is one significant aspect for the variability of number of calls stated by Dalton, Holmes and Silfkin. This aspect is the mean number of calls that are handled in an area. The more calls there are in an area the lower the variability in number of calls is (Dalton, Holmes, & Slifkin, 2003). If this is translated to an ambulance model, it says that the number of calls in a crowded area has a lower variability than the number of calls in a non-crowded area. This means that it is harder to model an ambulance network for non-crowded areas.

In the figure beneath a forecast based on historical numbers of the percent of ambulance calls by zone in Canada is shown. This forecast is used to determine how many ambulances should be available on a central point in every zone.

FIGURE 2: PERCENT OF AMBULANCE CALLS BY ZONE (SWOVELAND, UYENO, VERTINSKY, & VICKSON, 1973)

When these forecasts are made, the model can be formulated. The next big challenge in formulating the model is the geographical aspects. It is stated that travel time for one kilometer in a crowded city is larger than in a small town since an ambulance has to deal with more traffic in a crowded city. (Groom, 1977) Not only traffic is a geographic issue in modeling an ambulance network, also the quality of roads for instance influence the response time for a call.

The last challenge for modeling a good ambulance network is the tension between costs and ethics. A paramedic unit is extremely costly (Mayer, 1980) so it is not possible to put one on every corner of a street. The question is which chance of exceeding the 15 minutes is acceptable. When this is translated the hard way it says for which additional costs it is acceptable to waste a life. Research has shown that every minute an ambulance on average gets at an urgent scene sooner the number of lives saved increase (Mayer, 1980) so the more ambulances there are the

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more lives are saved. To determine the acceptable exceeding-time there are targets set by the Dutch government. This target says that 95% of the urgent calls should be reached within 15 minutes. This target meets the requirements of Bevan and Hamblin (2009) that a target must be commonly known and annually measured. Meeting a target to handle urgent calls within 15 minutes cannot be 100% guaranteed, since the ambulance network is not the only variable that influences this time. In 20% of the fatal road accidents in Missouri the caller caused a delay of five minutes or more by giving vague descriptions of accident and location. (Brodsky, 1992)

3.4 OTHER VARIABLES

Besides the operational model of the ambulance there are some side factors which are important when an ambulance network is set up. First in the Netherlands not only ambulances are used to fulfill the service calls from patients. For highly urgent calls or calls in an area that is difficult to reach, helicopters can be used. These helicopters are stand-by at several hospitals in the Netherlands. The use of a helicopter is much more expensive than the use of an ambulance, so these helicopters are only used when it is highly necessary. Therefore the helicopters do not influence the coping with unpredictable demand in the Netherlands. There are no mountains or other geographically unreachable areas that exclusively need a helicopter. An ambulance network is able to serve every corner of the country. Only in special situations a helicopter is needed. For instance when a major traffic incident happens on the highway which results in a big traffic jam that makes it impossible for ambulances to reach the victims.

A second possibility to reduce the response time is the use of motorcycles. These motorcycles are only meant for calls that do not require a patient to be taken to a hospital, or for calls that require urgent first aid after which an ambulance can come and pick up the patient. This makes the use of motorcycles a very interesting option. Since it is impossible to investigate the effort of using motorcycles in this thesis, its quantified effect on the problem statement is not taken into account. An ambulance is still defined as a car with equipment and qualified personnel. The use of motorcycles is however a serious option to cope with unpredictable demand, since motorcycles are fast and can easily cope with geographical- and infrastructure problems like a traffic jam.

A third factor that is important when the minimal number and placement of ambulances in a network is determined are big events. The chance of receiving an emergency call increases significantly when a big event takes place (Baker & Fitzpatrick, 1986). These events are certainly important to take into account when an ambulance network is set up. However these events do not influence the problem statement, which searches for ways to cope with dealing with unpredictable demand in an ambulance network. A big event just requires a specified number of extra ambulances which are available in case of emergencies.

3.5 CONCLUSION

The performance of an ambulance network is influenced by a number of factors. While the physical network (ambulance with qualified personnel and equipment) determines the costs of an ambulance network, the performance is also influenced by the technology system, the aspects of the process, geographical aspects and infrastructure. Also a number of other variables that are not relevant for this investigation influence the performance of the ambulance network.

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4. AMBULANCE NETWORKS AND UNPREDICTABLE DEMAND

This chapter combines the propositions of dealing with unpredictable demand in operations management with the characteristics of an ambulance network. To do this the propositions of dealing with unpredictable demand have to be translated to ambulance modeling. The propositions are drawn from a situation where a company produces products, while an ambulance network can be seen as a service company. This because a comparison like this has not been made in existing theory yet, so it might give new insights. This translation and the comparison with ambulance networks is made in this chapter.

4.1 PROPOSITION 1

The first proposition of dealing with unpredictable demand in operations management was: “Holding finished-goods inventory is a solution to make sure that unpredictable demand can be met.” Holding inventory of finished-goods can in an ambulance be seen as increasing the number of ambulances, which includes equipment and qualified personnel. When the number of ambulances and staff is increased, the chance that there is an ambulance available when a call is made also increases.

As was stated in chapter 2, holding inventory of finished-goods is not seen as a perfect solution since inventory is expensive. This is even more applicable in the case of an ambulance network (Mayer, 1980). The reason why this effect is stronger in the case of an ambulance network is the personnel. Finished-goods inventory is inventory that is stored in a warehouse, while an extra ambulance also requires two extra persons to drive the ambulance and provide medical treatment. This effect is even increased by the fact that an ambulance network has to be available 24 hours a day and seven days a week. Working time during nights is more expensive than working time during days.

Still this first option to cope with unpredictable demand is widely used. There has to be a balance between response time and costs (Swoveland, Uyeno, Vertinsky, & Vickson, 1973). The government has to determine when the costs of an extra ambulance outweigh the risk of not being able to save a life because of a too long response time. A variation on this option is to change the type of equipment, as was already described in chapter 3. It is possible to use motors for certain emergency calls, which are faster than ambulances. The costs and effort such an option provide should be analyzed to define if this option is effective and efficient in the Netherlands.

4.2 PROPOSITION 2

The second proposition of dealing with unpredictable demand in operations management was: “A flexible process design is a solution to be able to produce more or less in order to fulfill unpredictable demand.” This option gives more possibilities to increase the performance of an ambulance network. This performance is again the percentage of urgent calls that are handled within 15 minutes (Bevan & Hamblin, 2009). Erkut has developed a model to combine existing location models with survival chance models. (Erkut, Ingolfsson, & Erdogan, 2008) Part of this investigation is the theory that is commonly used to design an ambulance network. This queuing theory does however not totally optimize the possibilities to work most efficient. To understand this, the queuing theory is explained in the most basic way, starting with a figure from Hillier and Liebermann (2005).

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Input Source QueueService

MechanismCustomer Served Customer

Queueing System

FIGURE 3: THE BASIC QUEUEING PROCES (HILLIER & LIEBERMANN, 2005)

The process starts with a customer (in this case a patient) that arrives at the queuing system (in this case calls for an ambulance). This customer has to wait in the queue for a waiting time that varies. At certain times the customers are served by a service mechanism that is mostly called a server (in this case the ambulance arrives at the patient). This model has two probability aspects that can be simulated. First the arrival time of customers can vary and secondly the time in which a server is busy with a customer can vary. The first aspect is in the ambulance case the unpredictable demand and the second aspect is the time it takes to arrive at the patient after a call.

In this theories the locations of ambulance bases are combined with the districting of response segments. (Iannoni, Morabito, & Saydam, 2009) This model fits the ambulance model and is therefore used in all the theory about ambulance network calculations. (McAleer & Nagvi, 1994) (Brotcorne, Laporte, & Semet, 2003) (Singer & Donoso, 2008) (Taylor & Templeton, 1980) This theory can predict the chance that the arrival time for an urgent call is within fifteen minutes. It has however one short coming; it uses fixed service mechanisms. This complies for most applications of the model, but for an ambulance network it does not. An ambulance network can be flexible (Andersson & Värbrand, 2007).

Andersson and Värbrand introduce a new way of modeling ambulance networks that overlays with the second proposition, which goes about flexibility. The queuing theory is not a flexible process design, but a flexibility factor can be introduced in this model (Andersson & Värbrand, 2007). Where most theories talk about availability as an important aspect of an ambulance network, Andersson and Värbrand rather call this aspect “preparedness”. They state that an ambulance should not only be available in a network but that the network also has to be prepared to emergency calls on any place. They propose a flexible network where the preparedness is always sufficient.

This sufficiency can be reached by making ambulances the starting point instead of ambulance posts. Ambulances should not start from a fixed ambulance post, but make sure the preparedness is guaranteed I every section. For instance when an ambulance in section A is called, the preparedness in this section decreases. When an ambulance from section B moves a few kilometers in the direction of section A, the preparedness can be assured. This way when another call is placed in section A, the ambulance of section B can be in time at the call (Andersson & Värbrand, 2007).

This option is not used in the Netherlands where ambulances start from a fixed ambulance post, but certainly provides options to increase the performance of the ambulance network. This means that more urgent calls can be handled within 15 minutes without an increase in costs. With the current technology system as described in chapter 3, the person in the call center that calls an ambulance could also call another ambulance to move into a certain direction.

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4.3 CONCLUSION

When the ways of dealing with unpredictable demand in operations management are applied to the ambulance network, two possibilities to deal with unpredictable demand in ambulance networks appear. At first the number of ambulances and qualified staff can be increased. The chance that there is an ambulance available when a call is made increases with the number of ambulances and qualified staff. This is an expensive solution.

Second the ambulance model could be made more flexible. The queuing theory uses static service mechanisms, while an ambulance cannot be called static. When ambulances do not have fixed start locations but are dynamically localized to assure maximum preparedness, the performance of the ambulance model increases without addictive costs.

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5. CONCLUSIONS AND DISCUSSION

In this chapter the conclusions of this thesis are briefly named and discussed for the Netherlands. Also options for further research are provided.

5.1 CONLUSIONS

In this thesis the problem statement: “How should an ambulance network cope with unpredictable demand, in order to meet the required performance with minimum costs” is analyzed with a literature investigation. The focus of this investigation was to combine solutions that are used in operations management with the specific aspects of an ambulance network. From operations management two ways of dealing with unpredictable demand were stated to be useful for the case of an ambulance network. At first holding finished goods to be able to adjust to higher demand is a used possibility which led to proposition 1:

“Holding finished-goods inventory is a solution to make sure that unpredictable demand can be met.”

In the case of an ambulance network this would mean that increasing the number of ambulances with equipment and qualified workers would make it possible to handle an increase in emergency calls. As stated this solution has to balance the costs with the need for new ambulances with equipment and qualified people. A decision should be made when an increase in costs outweigh the chance of a life being saved.

The second solution from operations management is to work more flexible and be able to adjust to varying demand. This led to proposition 2:

“A flexible process design is a solution to be able to produce more or less in order to fulfill unpredictable demand.”

In the case of an ambulance network this would mean that ambulances should do more work with the same number of ambulances. Research showed that ambulance networks in the Netherlands are set up according to the queuing model. This model is a static model that cannot cope with flexibility. Andersson and Värbrand showed a way of adjusting this model to make it more flexible. Ambulances should not be seen as static service mechanisms but as dynamic service mechanisms. When an ambulance is adjusted to a call, another ambulance has to move in the direction of the first ambulance his section to keep the preparedness on a high level. This means ambulances should not start from a fixed starting point, but should be dynamically divided over the country to optimize the preparedness.

The perfect solutions to the problem statement is a mix from both solutions. A dynamic model should be created with enough ambulances to assure that all urgent emergency calls can be handled within the prescribed fifteen minutes. There will always be a probabilistic degree, since an ambulance can get a flat tire or hit a traffic jam, but there are possibilities to make the Dutch ambulance system more dynamic to handle more urgent calls within 15 minutes. The proposed combination of employing enough ambulances in a dynamic ambulance network to meet the targets leads to the demanded performance with minimum costs.

5.2 DISCUSSION

In the literature contradictories between the solutions to create a perfect ambulance system exist. There are writers that state that the missing of targets in ambulance networks is caused

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by variation in demand (Baker & Fitzpatrick, 1986) (Dalton, Holmes, & Slifkin, 2003). There are however also writers that see demand as a variable that cannot be understood and focus on the chancing the ambulance network to be able to meet this demand. (Andersson & Värbrand, 2007) (Fitzsimmons, 1973). The truth is probably somewhere in the middle. The demand is varying without a known pattern, but big parts of it can be forecasted. For instance in summer more emergency calls come from old people that cannot stand the heat. It is hard to predict the variation, but there are possibilities for the Netherlands to increase the performance of an ambulance network by predicting demand. Where the performance of an ambulance network is the percentage of urgent calls that are handled within 15 minutes. As stated in this thesis there are also possibilities for the Netherlands to increase this performance by chancing the ambulance network. For instance by making it more flexible.

This thesis is based on articles from important journals. This increases the credibility of the theories. It must however be said that most articles where literature articles. In some articles models were developed to forecast the performance of an ambulance network. Empirical data however is very rare. Because demand is varying with an unknown pattern, it is hard to test hard hypotheses for an ambulance network. Most developed models are tested by try and error methods. As long as they are sufficient to meet the targets they are used. If not, possibilities to change the model are tested and implemented and the circle starts all over again. Most of the times such a change considers just changing an ambulance post (service mechanism) by adding ambulances with equipment and qualified personnel or by changing the location (Baker, Clayton, & Taylor, 1989). As stated the challenges in healthcare and especially for ambulance networks are scale, complexity and change. (Harper & Pitt, 2004)

A third remark is that for the Dutch ambulance network numbers from the RIVM are used. The 20% missing targets that is caused by unavailability of ambulances (chapter three) is drawn from a press release of the RIVM. (RIVM, 2003) Because the RIVM likes to state this percentage as low as possible, it is likely that this percentage is in reality higher. Since this only increases the strength of this thesis, the importance of the thesis becomes clear. Reducing a part of the 20% of the urgent calls that are not met within 15 minutes will increase the number of survivors since there was already stated that reducing the response time significantly increases the chances of survival according to Brodsky and Hakkert. (Brodsky & Hakkert, 1985) This makes that the thesis has practical relevance.

5.3 RECOMMENDATIONS

This research offers possibilities for more and deeper research practical and academic. It provides a theoretical framework of possibilities and states what seems to be the best solution. It is definitely worth the trouble to do some empirical research. Hard facts that show how a change in an ambulance model changes the performance of this model are very rare but seem to be very important. To describe a better way of dealing with the ambulance network problems is not enough to convince policy makers to really change the network. Since it is a matter of life and death, decisions about this system are very sensitive. Academically there is also research that can be based on this thesis. The thesis views the problem from the supply side. Increasing the way of predicting the number of calls in a section is left out of the research. This is however an important variable when the performance of an ambulance network should be increased. The easiest way to deal with variation is eliminate the variation. In this thesis the variation is seen as a fact, but there might be ways to reduce or forecast the variability. A combination of perfection on the supply side and perfection on the demand side would create a perfect situation, as far as there can be spoken of perfect situations when it concerns emergency calls.

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6. REFERENCES AND OTHER SOURCES

6.1 REFERENCES

Andersson, T., & Värbrand, P. (2007). Decision support tools for ambulance dispatch and relocation. Journal of the Operational Research Society, Vol. 58 , 195-201.

Athey, S., & Stern, S. (2002). The Impact of Information Technology on Emergency Health Care Outcomes. The RAND Journal of Economics, Vol. 33, No. 3 , 399-432.

Baker, J. R., & Fitzpatrick, K. E. (1986). Determination of an Optimal Forecast Model for Ambulance Demand Using Goal Programming. The Journal of the Operational Research Society, Vol. 37, No. 11 , 1047-1059.

Baker, J., Clayton, E., & Taylor, B. (1989). A Non-Linear Multi-Criteria Programming Approach for Determining County Emergency Medical Service Ambulance Allocations. The Journal of the Operational Research Society, Vol. 40, No. 5 , 423-432.

Bevan, G., & Hamblin, R. (2009). Hitting and missing targets by ambulance services for emergency calls: effects of different systems of performance measurement within the UK. Journal of the Royal Statistical Society, Vol. 172, Part 1 , 161-190.

Brodsky, H. (1992). Delay in ambulance dispatch to road accidents. American journal of public health , vol. 82, iss. 6, p. 873-876.

Brodsky, H., & Hakkert, A. (1985). Accessibility and Bystander Response in an Emergency. Transactions of the Institute of British Geographers, New Series, Vol. 10, No. 3 , 303-316.

Brotcorne, L., Laporte, G., & Semet, F. (2003). Ambulance location and relocation models. European journal of operations research , vol. 147, nr. 3, p. 451-463.

Dalton, K., Holmes, M., & Slifkin, R. (2003). Unpredictable Demand and Low-Volume Hospitals. North Carolina : North Carolina Rural Health Research and Policy Analysis Center.

Erkut, E., Ingolfsson, A., & Erdogan, G. (2008). Ambulance location for maximum survival. Naval Research Logistics , 42-58.

Fitzsimmons, J. A. (1973). A Methodology for Emergency Ambulance Deployment. Management Science, Vol. 19, No. 6 , 627-636.

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Gaither, N., & Frazier, G. (2002). Operations Management. Ohio: Thomson Learning. Groom, K. (1977). Planning Emergency Ambulance Services. Operational Research Quarterly, Vol. 28, No. 3, Part 2 , 641-651.

Harper, P., & Pitt, M. (2004). On the Challenges of Healthcare Modelling and a Proposed Project Life Cycle for Successful Implementation. The Journal of the Operational Research Society, Vol. 55, No. 6 , 657-661.

Hillier, F., & Liebermann, G. (2005). Introduction to Operations Research. Boston: McGraw Hill.

Iannoni, A., Morabito, R., & Saydam, C. (2009). An optimization approach for ambulance location and the districting of the response segments on highways. European Journal of Operational Research , 528-542.

Mayer, J. (1980). Response Time and Its Significance in Medical Emergencies. Geographical Review, Vol. 70, No. 1 , 79-87.

McAleer, W., & Nagvi, I. (1994). The relocation of ambulance stations: A succesful case study. European journal of operational research , vol. 75, nr. 3, p. 582-588.

Repede, J., Jeffries, C., & Hubbard, E. (1993). ALIAS: A graphical user interface for an ambulance location model. International journal of operations and production management , vol. 13, iss. 12, p. 36-47.

Singer, M., & Donoso, P. (2008). Assessing an ambulance service with queuing theory. Computers and Operations Research , vol. 35, p. 2549-2560.

Slack, N., Chambers, S., & Johnston, R. (2009). Operations Management. New Jersey: Prentice Hall.

Swoveland, C., Uyeno, D., Vertinsky, I., & Vickson, R. (1973). Ambulance location: A probabilistic enumeration. Management Science, Vol. 20, No. 4 , 686-698.

Taylor, I., & Templeton, J. (1980). Waiting Time in a Multi-Server Cutoff-Priority Queue, and Its Application to an Urban Ambulance Service. Operations Research, Vol. 28, No. 5 , 1168-1188.

6.1 OTHER SOURCES

RIVM. (2003, 01 16). Persberichten. Retrieved from http://www.rivm.nl/persberichten/pb01-03.jsp

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7. APPENDIX: E-MAIL FROM THE RIVM

From:

Sent: Thursday,April 29, 2010 10:19 PM

To:

Subject: RE: Vraag over ambulances in Nederland

Beste Bart,

dat klinkt als een interessant onderzoek. Ik weet niet precies wat het is aan de modellering wat je onderzoekt, maar het RIVM zou graag je scriptie lezen als je interessante resultaten vindt.

Om op je vragen terug te komen. Het Nederlandse model is inderdaad gebaseerd op de Queuing Theorie, waarmee locaties vastgesteld worden waar ambulances het best kunnen staan om een optimale dekking te garanderen. De responsietijd van 15 minuten is nog steeds actueel.

Met vriendelijke groet,

Harald Wychgel

From: Bart Verhoeven

Sent: Tuesday, April 27, 2010 3:11 PM

To:

Subject: Vraag over ambulances in Nederland

Beste meneer/mevrouw,

voor mijn scriptie aan de universiteit van Tilburg doe ik momenteel een onderzoek naar modellering

van ambulance systemen. Om dit toe te passen op Nederland, wilde ik u wat vragen over het huidige

ambulance systeem.

In de theorieën die ik vind zijn ambulance modellen vaak opgesteld volgens de Queuing Theorie met

vaste locaties waarvan de ambulances vertrekken. Is dat in Nederland ook zo?

Tevens vroeg ik me af of de norm van 15 minuten responsietijd voor urgente oproepen nog steeds

actueel is.

Ik hoop dat u me kunt helpen met deze vragen.

Met vriendelijke groet,

Bart Verhoeven