Increasing the efficiency of an outpatient clinic A design science study at the UMCG

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Increasing the efficiency of an outpatient clinic

A design science study at the UMCG

Master Thesis

Msc. Technology & Operations Management

&

Msc. Supply Chain Management

University of Groningen, Faculty of Economics and Business

28 January, 2018

Z. van Ittersum

S2395975

z.van.ittersum@student.rug.nl

Supervisor University of Groningen

prof. dr. ir. C.T.B. Ahaus

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Abstract

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1. Introduction

The healthcare sector has changed rapidly to deliver healthcare more efficiently and effectively (De Vries, 2011). The need for high quality care is increasing because people live longer and more treatments are available. This is one of the reasons that the total costs for healthcare will rise with 26,3 billion euros in 2025 in the Netherlands if no measures are taken (NOS, 2018). Still a lot of healthcare processes are poorly designed and are characterized by unnecessary duplication of services as well as long waiting times and delays for patients (Ahmed, Manaf, & Islam, 2013). The Internal Medicine Department (IMD) at the University Medical Centre Groningen (UMCG) is no stranger to these inefficiencies and has to address the issues of rising costs and the increasing need for high quality care as well. This thesis addresses these problems by examining new ways to design healthcare services at the UMCG.

The IMD looks for opportunities to improve outpatient clinic processes that are characterized by inefficiencies and long waiting times. The main problem that they face is that Laboratory test results are frequently unavailable when patients arrive for their consultation with a physician. This causes inefficiencies and a decrease in the care quality for the following three reasons. Firstly, critical information during the consultation is not available, which decreases the effectiveness and efficiency of the consult. Secondly, the lab test results have to be communicated to the patient over the phone, which causes unnecessary follow-up work. Thirdly, once the lab test results become available and indicate that a change in medicine prescription would be beneficial, this has to be communicated over the phone. Physicians sometimes decide not to update the medicine prescription because it is hard to explain over the phone. They rather have the patient take the non-optimal medicine prescription correctly, than the optimal medicine description incorrectly.

The unavailability of lab test results during consults causes the subsequent follow-up work: the patient dossier has to be read again, newly available lab test results have to be judged, calling patients, referring patients to another specialist or making new appointments, and reporting/making notations. Calling patients is a time intensive task for the following two reasons. Firstly, patients do not always pick up the phone immediately. Secondly, it is hard to communicate complex matters clearly over the phone, therefore it takes time to communicate implications of lab test results over the phone. The costs of follow-up work have been calculated to be as high as 104.000 euros per year and are perceived by physicians as inefficient and annoying (Jong, 2016).

Patient friendliness is highly valued by the UMCG, because the UMCG does not want to burden patients any more than necessary - on top of their illness - by having to take a day off from work, making a strenuous trip because of impaired mobility, or making a long trip because they live far away from the UMCG. The UMCG therefore attempts to keep patient waiting time and the number of patient visits to a minimum. Patient friendliness is reflected in the literature by accessibility/convenience of healthcare services which is a dimension of the patient satisfaction construct from (Ware, Snyder, Wright, & Davies, 1983). The convenience of healthcare services is used to represent the concept of patient friendliness in the rest of this paper. Patient convenience is defined as the factors involved in arranging medical care, the time and effort spend to get an appointment, the ease of reaching a care location, and waiting time at the care location (Ware et al., 1983).

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6 There are two fundamental solutions to make more lab test results available during consults, decreasing processing times drastically is one, increasing the time window to process the lab tests is the other. Because the former solution is not feasible, the UMCG is left with the latter. In the current way of operating this would mean that, the patient has to visit the hospital two times for research that takes more than a day to process; one visit to drop off the specimen, and one visit to have the consult. For tests that take a few hours to process a single visit could suffice for dropping of the specimen and having the consult, but this would result in long waiting times. This solution puts the UMCG at odds with their goal of being patient friendly. Therefore another solution is needed because it is paramount to increase the efficiency and quality of the consults. However, the solution should not come at the cost of patient convenience, by increasing the burden on patients. This leads to the following research objective:

To design a system that increases the availability of Laboratory test results during the consults at the Internal Medicine Department.

A DSR approach will be used in this study, DSR fits well because there is a practical problem at hand, for which a solution is needed in the form of a new design. A DSR approach can design useful things from which scientific knowledge can be produced (Wieringa, 2009). Starting with reviewing the literature to define the problem and identifying a research gap. Thereafter the following steps of the regulative cycle will be applied: (1) system description, (2) system analysis, (3) design, and (4) testing (Van Strien, 1997). The DSR approach enables one to combine practice and scientific knowledge, exactly what is intended in this research.

The new design has to take patient convenience into account, which is related to the distance patients have to travel to the hospital and the mobility of patients in this paper. Another important factor is the longest processing time, which concerns those types of lab tests that take the most time to process on average. For the Laboratory to start processing the tests ordered for a consult, specimen is needed. This specimen is dropped off at the same time, which causes the tests with the longest processing time to be the bottleneck. At the hand of the longest processing time, distance, and the mobility of the patient, this design should be able to decide where and when the specimen has to be dropped off.

The longest processing time indicates the time window necessary for the Laboratory to process the lab tests needed for the consult in time. The time window needed for the Laboratory creates a time buffer seen from the patient perspective, because after the specimen has been dropped off, the patient has to wait both for the diagnoses and the treatment plan. The time buffer from the patient perspective fits with the following definition of Roemeling, Land, Ahaus, Slomp, & van den Bijllaardt (2017), time buffers in healthcare most commonly relate to the patient either waiting for treatment or diagnosis. According to Klassen & Rohleder (2004) the amount of time a patient has to wait is an important aspect for the quality of an outpatient service. Chen et al (2010) support this observation by stating that long waiting times lead to complaints and major dissatisfaction of medical care delivery among patients.

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7 convenience is increased by introducing local BSUs in the new design, which makes it more convenient for the patients to drop off their specimen. Thereby the IMD can increase the availability of lab tests during consults while keeping in check with the UMCG guidelines for being patient friendly.

According to Hopp & Spearman (2008) are buffers a logical consequence to deal with variability, that degrades the performance of a production system or service. Variability has to be buffered by time, capacity or inventory (Hopp & Spearman, 2008). Another type of buffer is quality, it sacrifices quality of the service to deal with variability (Hopp & Spearman, 2008; Joosten, Bongers, & Janssen, 2009). The convenience buffer is most similar to the quality buffer, where the latter uses quality to influence processing times, the former uses the convenience aspect of quality to improve the care process. The theoretical contribution of this paper is therefore the introduction of a new type of buffer – the convenience buffer and how this buffer can be applied.

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2. Problem statement and research design

The problem that the UMCG faces will be described in this chapter. Based on this description an applicable methodology is chosen.

2.1 Problem background

According to Tolga Taner, Sezen, & Antony (2007) healthcare is a vast web of complexity that has huge advances in technology and treatment, but it has many inefficiencies, errors, resource constraints and other issues that jeopardize the safety and accessibility of patient care. The outpatient clinic of the IMD is no stranger to complexity and inefficiencies.

A research done at the Transplantation department (which is a subcomponent of the IMD) showed that physicians spent a lot of time on calling patients after consults (follow-up work) (Jong, 2016).The follow-up work resulted from incomplete consults, due to lab test results being unavailable during consults. Lab test results contain critical information which physicians need to decide on the treatment for their patients. When the results do become available, physicians have to look into the patient files again and put the treatment together accordingly and inform the patient by phone. This took physicians 10 minutes of follow-up work on average per consult at the Transplantation department (Jong, 2016). Other departments of the IMD experience the same problem of lab test results being unavailable during consults, which in turn caused a significant amount of follow-up work. An outpatient clinic shift consists of multiple consults with different patients, about 10-15 patients per outpatient clinic shift. There have been used 1734 request codes by the IMD and 461853 lab test results have been reported for just two regular months. These different request codes refer to distinct research requests and have distinct average processing times. This shows the immense complexity that has to be dealt with. Currently these processing times are not taken into account when planning the consults.

The mobility of patients and the distance patients live from the hospital in combination with the UMCG striving to be patient friendly adds complexity to the problem. It puts a constraint of patient convenience on the new design, which essentially means that the UMCG does not want patients to take more effort attaining their medical services in the new design than in the old design. Otherwise the solution would be simple, just let the patients deliver the specimen to the UMCG BSU days or weeks before the consult, thereby giving the Laboratory a sufficient time window to process the specimen in time for the consult. From some patients it cannot be expected to make two trips to the hospital because it takes them a lot of effort and energy because their mobility is impaired. They might be old and have no driver’s license, they might be exhausted by their illness or travelling might be hard due to financial issues to name a few reasons. From other patients it cannot be expected to make two trips because they live at great distance from the hospital, which would result in a significant amount of extra travelling time, this could result in them having to take half a day off or an entire day off work, which is very inconvenient.

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2.2 Research objective

The IMD wants a new design for their outpatient clinic to improve their currently inefficient way of working, get rid of the huge amount of follow-up work and the frustrations it creates among physicians.

Currently the specimen is dropped off just a few hours before the consult, while some tests take several hours or even days to process, therefore the Laboratory cannot process the lab tests in time for the consult. Without the lab test results being available, the consult cannot be completed in many cases, and has to be finished by phone later on, which is the source for the follow-up work. This results in the following objective; to create a design that increases the availability of lab test results during IMD outpatient clinic consults.

The key to completing this objective is getting specimen to the Laboratory days or weeks before the consult dependent on the longest processing time. Which means for a substantial amount of patients that dropping off the specimen and the consult have to be on separate days, instead of on the same day, this is expected to decrease patient convenience. The IMD sees an opportunity to improve patient convenience by taking in specimen closer to where the patient lives, to compensate the decrease in convenience from the two days visit. By making use of local BSUs that decrease the traveling time of many patients for the action of dropping off specimen. The incorporation of local BSUs is therefore an important aspect of the new design that will be investigated.

Another major factor is the longest processing time, which refers to a single research request that is needed for a single consult with the longest processing time. The longest processing time will be the bottleneck for the following two reasons; specimen is dropped off at the same time and the processing of lab tests can only start after the specimen has been dropped off. Therefore the moment the processing of different lab test requests for a single consult can begin is the same, resulting in the lab test with the longest processing time being the bottleneck. The end result should be a new design that plans consults in a way that leaves sufficient time between dropping off specimen and the consult. This design should have rules for deciding where patients have to drop off specimen, locally or at the UMCG. These rules take the distance from the hospital and the mobility of the patient into account. The current system has to be adapted to the new way of working, because normally the patient and the specimen would arrive on the same day. Requirements for implementation of this new design should become clear.

Longest processing time

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Planning instructions

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Planning

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Categorization

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10 In Figure 1 a black box representation of the new design is given, to outline the features that will be designed. A black box is an entity where one knows the input but does not know how this input will be transformed into an output. Still, this representation provides clarity on the topics that will be examined and designed in this paper. Namely the following four black boxes: the categorization mechanism, the drop off location determination mechanism, the planning mechanism and the bigger black box that represents the interaction between them. The arrows on top and on the right are the inputs for the new design and the arrows on the right and bottom are the output of the new design. These inputs go into a black box which contains three smaller black boxes, the drop off location determination mechanism, the categorization mechanism and the planning mechanism. Because it is not yet clear how these smaller black boxes will interact with one another they are placed inside the bigger black box. For the similar reason the three mechanisms are placed in a black box since it is not clear what they will contain, how they will function, and how they will interact with one another.

The function of most black boxes are rather self-evident, except for the categorization mechanism which needs some clarification. The categorization mechanism is included mainly to distinguish between the different types of patients and how they should be planned. In table 1 there is a summary of all the possible categories based on three binary dimensions; distance, patient mobility and the processing time. The impacts of these three factors on the lab test being available during the consults and if a single or double visit has to be planned are given in the last two columns. The new design should be able to categorize the patients into these categories and plan them accordingly.

Distance

<30 km Patient mobility impaired

Processing

time Lab tests available during consult Single or double visit

Yes Yes Long No Single

Yes No Long Yes Double

Yes No Short Yes Single

Yes Yes Short Yes Single

No Yes Long No Single

No Yes Short Yes Single

No No Long No Single

No No Short Yes Single

Table 1: Patient categories

2.3 Research design

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11 The regulative cycle can be described in short as follows; it starts with investigating a practical problem, then specifies solution designs, validates these, and implements the new design; the outcome which can be evaluated, can be the input for a new turn through the regulative cycle (Wieringa, 2009). The regulative cycle is the general structure of a problem solving process; analyze the current situation and identify the current change goals, propose possible changes for achieving the goals, evaluate the changes, apply the changes and start the cycle again if necessary (Wieringa, 2009). In terms of the regulative cycle, design science can be seen as solution proposals to practical problems which will be designed and validated (Wieringa, 2009). Because the UMCG essentially requested a proposal for a solution to a practical problem - as can be derived from the introduction - the regulative cycle will be used because it fits with this request. The regulative cycle consists of the following steps; (1) system description, (2) system analysis, (3) design and (4) testing. These steps will be described briefly hereafter.

2.3.1 System description

To get to the core of the problem; the current system will be described in detail and every step in the process should become clear. Therefore an oversight of the process will be provided at the hand of a process map. Also a short description of every department involved will be given, to understand the role they play in the current system. The system description will be used as input for the next phase where the bottlenecks and the source of these bottlenecks will be identified.

2.3.2 System analysis

With the input from the system description the sources of the problems can be diagnosed in the system analysis, this should reveal the flaws of the current system. The differences between departments will be analyzed in terms of patient statistics, i.e. age, quantity, and the distance patients live from the hospital. The insights gained from the system analysis will be used as input for the next phase, the design phase. Finally, requirements for the new design will be identified and serve as input for the design phase.

2.3.3 Design

The design phase identifies the solution to the problem by creating an artefact. This artefact can currently be seen as the black box from Figure 1 where the main inputs are the critical processing time, distance, and mobility. These inputs should be transformed into the following outputs: a place to drop off specimen and planning instructions. The design phase should unravel these black boxes and show how they could be designed and what they consist of. Components like a drop off location determination mechanism, a planning mechanism, a categorization mechanism, and the interaction between these mechanisms will be designed and their properties thoroughly discussed. The interaction between these components is of utmost importance and should therefore be represented clearly and schematically. It should become clear how patient groups are classified and what the policies to plan these different groups are like.

2.3.4 Testing phase

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2.3.5 Data sources and collection

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3. Theoretical Background

The information from laboratories accounts for 60-70 percent of all information used in clinical decision making (Fowler, Martin, & Spence, 2005; in Harrison & McDowell, 2008). This shows that information from laboratories is critical in healthcare, therefore decisions about changing the design of processes that deliver Laboratory information should not be taken lightly. The aim of this literature review is to deepen the knowledge about care processes in outpatient healthcare settings. This enables one to better understand and identify bottlenecks that slow down the process and could point in the direction of a solution.

3.1 Variability

This subchapter will discuss variability in healthcare and explains certain constructs by which it has been successfully analyzed. The paper of Litvak & Long (2000) suggests that there are possibilities to improve the efficiency in healthcare systems by focusing on the reduction of variability. Hopp & Spearman, (2008 p.295) affirm this by stating as a general law: “that

increasing variability always degrades the performance of a production system”. Variability

has been divided into the following two constructs: artificial variability, and natural variability (Joosten et al., 2009; Litvak & Long, 2000).

3.1.1 Artificial variability

Artificial variability is often caused by dysfunctional management and can be seen as actions or processes that unnecessarily increase the cost and inefficiency that can be controlled (Litvak & Long, 2000). In other words, it is variability that could have been mitigated by good design and management of healthcare operations (Joosten et al., 2009). Lead-time can increase through variability and processing times tend to increase by variability as well, because variable processes are harder to standardize. Artificial variability can be caused by not understanding a process thoroughly. This lack of information can result in a design or way of operating that creates variability that is unnecessary. When a process is fully understood, optimal design and operating procedures can be construed that minimize variability and thereby increase performance.

3.1.2 Natural variability

Natural variability can be seen as processes or procedures that can and never be done the same way twice, it relates to uncontrollable factors (Joosten et al., 2009). Litvak & Long (2000) divided this natural variability up into clinical variability, flow variability and professional variability with regards to healthcare delivery systems. Clinical variability relates to the difference in the degree of illness, choice of treatment alternatives, and response of the patient (Litvak & Long, 2000 p.307). Flow variability relates to the random arrival of the need for care and patients (Litvak & Long, 2000 p.307). Professional variability is defined as the difference in the ability of healthcare professionals to deliver treatment (Litvak & Long, 2000 p.307). To summarize, healthcare delivery processes should be designed in a way that can cope with natural variability and minimize artificial variability.

3.2 Buffers

According to Hopp & Spearman (2008 p.295) another law with regards to variability buffering exists namely: “ variability in a production system will be buffered by some combination of

inventory, capacity or time”. According to Kallen, Terrell, Lewis-Patterson, & Hwang, (2012)

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14 Capacity buffers mitigate variability by enabling processes to speed up production by allocating the extra resources that are available in the buffer (Hopp & Spearman, 2008). Capacity buffers in healthcare can be seen as extra available personnel, like doctors and other specialists. Inventory buffers are for example stock or finished goods, which can be directly utilized when demand arrives (Hopp & Spearman, 2008). Inventory buffers are not applicable to services, because services cannot be stored, therefore inventory buffers are less applicable to healthcare service settings.

Time buffers are seen by Hopp (2011) as the time a production system has to wait to continue production or the time a customer has to wait for his service. Time buffers in healthcare most commonly relate to a patient either waiting for treatment or diagnosis (Roemeling et al., 2017). In this paper there are two types of time buffers; the time buffer seen from the patient perspective which relates to the definition of Roemeling et al (2017) and the time buffer seen from the perspective of the outpatient clinic which relates to the production system having to wait to continue processing (Hopp, 2011).

Quality is another buffer against variability, it sacrifices the quality of a service to deal with variability (Hopp, Iravani, & Yuen, 2007). Working harder, multitasking and truncating the work done for a customer are mechanisms by which the workload can be balanced (Batt & Terwiesch, 2017). Quality buffers are in essence processing time buffers where non-critical tasks are removed to decrease the processing time as a response to variability. Physicians have discretionary task completion, which means that the physician has influence over the means and time it takes to complete a task. This enables a degree of flexibility in the processing times, it allows physicians to adjust the quality of output to manage the workload (Hopp et al., 2007). According to Bombard et al (2018) there is a substantial amount of literature that indicates that engaging patients can lead to improved efficiency, quality of care and cost effective health service utilization. It would be interesting to see if the efficiency of outpatient healthcare processes could be improved by engaging patients more in the health services. To see if the engagement can lead to a decrease in flow, clinical and professional variability. This could be done by evaluating the tasks patients are responsible for in the care process, like dropping of specimen and being on time for a consult. This could have tremendous impact on the efficiency and quality of care processes. However patient engagement is traditionally focused on the relationship between providers and patient making care decisions and how to increase patients efforts in managing their own care (Bombard et al., 2018). This paper will take a different perspective and focus on opportunities for integrating patients in the care process of an outpatient clinic.

This integration of patients in the care process might have impact on the patient convenience of the healthcare service, the patient waiting time might increase which decreases the patient convenience for example. Therefore this paper proposes to introduce a convenience buffer, which can be used as tool to increase or decrease the patient convenience of a healthcare service, to improve the quality or efficiency of the healthcare service by changing the care process.

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3.3 Mobility and age

This subchapter shows the relation between age and mobility and how mobility can be assessed. Thereby grounding age as the predictor for mobility in literature.

According to Mollenkopf et al (2004) mobility is not only a basic human need for physical movement, but has become an important prerequisite for participation in one’s natural, social and cultural environment, because of spatial distance between activities in these environments.

The accumulation of deficits in aging people, like muscle weakness, reduced balance and neuromuscular abnormalities result in impediments to their mobility and may cause difficulties in activities of daily living (Bischoff et al., 2003). Mollenkopf et al (2004) support this by stating that with advancing age the risk of physical and sensory impairments increases. This means that as age increases travelling to the hospital or other healthcare services will become harder. Functional mobility assessments enable physicians to determine functional independence, by mimicking mobility efforts of everyday life (Bischoff et al., 2003). These functional mobility tests can be used to assess whether a patient is reasonably able to travel to a hospital or another health care service. The timed up and go test (TUG-test) is a useful method for assessing the mobility, because it is objective and easy to perform (Bischoff et al., 2003). The TUG-test consists of basic mobility skills, such as walking 3 meters, rising from a chair, turning and siting down on the same chair (Bischoff et al., 2003). This TUG-test or similar tests could be used to classify patients, these classifications could be used as input for planning decisions.

3.4 Conceptual model

The conceptual model in Figure 2 will be explained in this section, it will elaborate on the relations between the factors distance, mobility, convenience buffer, time window and availability of lab test results during consults.

Availability of lab test results during consults is operationalized as the percentage of lab results that is available during the consults with the patients in a certain time period. Meaning that when a physician has 10 patients consults and for 7 patients the lab test results are available during the consult, that the availability is 70%.

Distance Convenience buffer Availability of lab test results during consults Time window Mobility

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16 Distance refers to the distance a patient lives from the hospital. In this research it will measured by calculating the haversine distance between the UMCG and local BSUs and the zip-codes of the patients. The haversine distance essentially measures the distance between two points.

Mobility refers to the mobility of patients, this can include the financial mobility, physical mobility, and the mental mobility. Because patients need money, the physical capacity, and the mental capacity to be able to travel to the hospital. The mobility will be measured in this research by using age to categorize patients. As age increases the mobility will decrease in general (Bischoff et al., 2003; Mollenkopf et al., 2004). Therefore age is a useful heuristic to predict mobility, besides that it enables the usage of accurate data on patient age from the EHR. The time window in the conceptual model refers to the time between dropping off the specimen and the consult taking place. This is the time the Laboratory has to process and publish the lab test results.

The convenience buffer is a buffer that regulates the convenience of the healthcare service to improve efficiency or quality of this healthcare service. It is operationalized in this research as the change in patient convenience that comes with splitting up the tasks of dropping off specimen and having the consult on separate days and the impact of integrating local BSUs in the care process on patient convenience.

By splitting up the tasks on different days, the time window for the Laboratory to process lab tests increases and patient convenience decreases. Therefore the convenience buffer has a negative relationship with the time window, because as the convenience for patients decreases the time window increases.

The time window has a positive relationship with the availability of lab test results during consults, this is rather straightforward because as the time window increases, the time the lab has to process specimen increases, enabling them to process more lab tests in time.

The mobility of patients has a positive relationship with the availability of lab test results during consults because as patients are more mobile they have less trouble travelling and are therefore expected to be more willing and able to drop off specimen days or weeks before the consult. When patients are less mobile they are expected to be less willing and able to drop off specimen days or weeks before the consult because travelling is strenuous for them.

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4. System description

In this chapter a description of the current system will be given, starting with a global process map to get a feeling for how the system operates. Then the departments involved will be described in detail to gain understanding of the critical parts of the system.

4.1 System overview

Figure 3 shows a representation of the most important steps in the process. These steps will be clarified to understand the process.

The process can start with three types of patients. Firstly, a returning patient that has received a follow-up appointment during the last consult. Secondly, a referred patient, which is a patient that has been referred within the hospital itself and therefore already has a dossier in the EHR. Thirdly, a new patient that has been referred from outside the UMCG to the UMCG by another hospital or a general practitioner. These latter patients do not have a dossier yet and this has to be created, which takes extra time. The new patients are distributed by the triage physician of the department in question. For example, a patient with kidney complaints will be distributed by the triage physician of the Nephrology department. The physician that is most suited to handle the symptoms of the patient is subsequently assigned to this patient.

The next step is the same for every patient, the physician assigned to this patient will read the dossier and analyze the available information. Based on this analysis the physician orders lab tests that are deemed necessary, and makes his preliminary diagnoses and treatment plan. An order for a consult is placed as well, this order can include specific planning instructions for the medical administration, like dropping off specimen at least two hours before the consult. An employee of the medical administration will process this order by means of planning it on the date that the physician requested (+/- two weeks, depending on the availability of consult slots). When there is no slot available to plan the patient within the +/- two weeks, the employee will call the physician to discuss a solution. The planners also take into account the specific planning instructions of the physician if there are any. Planners try to plan proceedings (i.e. dropping of specimen, a scan, a consult) on the same day, to make the healthcare service as convenient as possible for the patient.

Then the medical administration will prepare a letter that invites the patients to the consult. This letter contains instructions for dropping off specimen like blood and urine, or instructions for undergoing scans and other proceedings. The letter can also include specific instructions for arriving sober for certain research.

Figure 3: Current process map Physician studies

the patient dossier, orders a consult, lab tests and other necessary research Returning patient Refered patient (Information is available in EHR) New patient Triage physician refers patient internally Patient receives letter with instuctions for dropping of specimen and doing

other proceedings

Patient arrives at the hospital and hands off specimen

at the blood sampling unit

Patient goes to and has the consult with

the physician

Order for the next appointment is put in the EHR Medical

administration plans the consult (+/-) two

weeks within expected date of

the consult

Medical administration makes and sends a

letter to the patient with instructions for the hospital visit.

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18 The patient will receive the letter and goes to the hospital and follows the instructions in the letter. This encompasses dropping off specimen in most cases but can also include undergoing a scan or undergoing other proceedings that the physician has ordered.

Subsequently, the patient will go to the outpatient clinic where the patient will have the consult and will register his arrival and wait until he/she is called to the consult. Then the patient has the consult with the physician. Normally there are 45 minutes scheduled for a new patient and 15 minutes for a returning patient. The physician will do an anamnesis and make a diagnosis based on the information gathered from the published lab tests and the anamnesis. This will result in a complete plan for treatment when lab tests are available during the consult or a preliminary plan for treatment when lab tests that contain critical information are unavailable during the consult. Then the doctor has to wait for these lab test results to be published and complete the diagnosis and treatment plan after the consult, and call the patient afterwards to inform them about the completed plan for treatment.

After the consult there are two options left. When the patient does not return, the involvement in treatment has ended from the department in question. The other option is that the patient gets a follow-up appointment which the physician orders at the end of the consult. The patient then enters the process again as a returning patient.

4.2 Departments oversight

This chapter will clarify the role of the patients and the following departments that are involved in specimen collection and processing: Medical Administration, IMD, BSU and the Laboratory. The role these departments play will be discussed to gain a better understanding of the process, by seeing the perspective of the different departments. It will clarify how much impact the departments and patients have on the availability of lab test results during consults.

4.2.1 Medical Administration

The employees of the Medical Administration plan the consults based on orders they receive from the EHR. They will plan consults based on the instructions given by the physician, like the date and time or specific instructions, like planning a patient on the latest time slot. They will then inform the patient by preparing and sending a letter. The Medical Administration has to plan the consult in a building that has several consultation rooms, however other outpatient clinics make use of these rooms as well, the Medical Administration is responsible for planning these consults as well. Therefore the employees have to take the interests of these other outpatient clinics into account, this limits the flexibility of planning consults. A notable phenomenon is that the outpatient consultation rooms are completely booked on Thursdays while there are a lot of unoccupied rooms on Friday. There is no apparent reason for this according to the employees of the Medical Administration.

4.2.2 Internal Medicine

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4.2.3 Blood Sampling Unit

The collection of specimen takes place in this department. The BSU takes in four types of specimen: blood, faeces, urine, and saliva. The BSU takes in approximately 60000 samples a year. The BSU has two locations where specimen can be dropped off in the UMCG. It operates on a first come first served basis. This means that a patient will be processed as soon as they are the first in line and have an order in the system to drop off specimen. The process of specimen collection takes 7 minutes on average. When the specimen is collected it is directly sent to the lab with a tube system and this where the role of the BSU ends in the process.

4.2.4 Laboratory

The Laboratory receives the specimen from the BSU and is responsible for processing this specimen and publishing the results of the lab tests in the EHR. The department carries out the Laboratory tests in the field of clinical chemistry, hematology and immunology for all medical departments in the UMCG. The Laboratory has the main role in the processing of Laboratory tests, however they have a minor impact on the availability of lab test results during consults because they cannot speed up processing of Laboratory tests. Because the only way to do this is to favor lab tests of the IMD department, or decrease batch sizes for certain lab tests. But this economically not viable and therefore the impact that the Laboratory has on the availability of lab tests during consults is minimal. Sometimes the physicians discuss the results with the Laboratory personnel, when the lab test results are ambiguous and the physician is not entirely sure what the results indicate.

4.2.5 Patient

The patient plays a major role in availability of lab test results during consults, because the patient has to deliver the specimen and thereby determines when the processing of lab tests can start. In the current system the patient will drop off the specimen just before the consult which leaves a small time window for the Laboratory to process the lab tests before the consult, this results in a lot of lab test results being unavailable during consults.

4.3 Patient and department statistics

In table 2 some statistics from 1 November 2017 - 1 November 2018 for the departments included in this analysis are shown, these statistics and their implications will be discussed in this subchapter.

From Table 2 a few interesting observations can be made, the departments are sorted from highest amount of patient consults to the lowest. There were a total of 57845 patient consults, the IMD is the biggest department by a substantial margin, while the Diabetes and Nephrology are the two smaller departments.

The Nephrology department has the highest average age, which indicates that illnesses treated here can be lived with till quite an old age, it also means that the average patient might be less mobile for this department.

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20 1 november 2017 -

1 november 2018

Amount of patient

consults Average age

Average distance from UMCG in km Median age Diabetes 8112 50,1 19,2 52 Nephrology 8445 58,5 35,5 61 Endocrinology 10721 50,8 32,9 52 Transplantation 11099 54,6 69,0 57 Internal Medicine 19468 53,9 31,0 55

Combined outpatient clinics 57845 53,6 37,6 56

Table 2: Patient statistics per department

The median age is higher than the average age for all departments this shows that more than fifty percent of the patients is older than the average age. The difference shows that there are quite a lot of old patients and that the group of young patients (0-40) pulls the average age down. This can be seen in figure 4 as well, it shows that the amount of patients gradually increases, than more steeply increases until it reaches its peak at 62 and then gradually declines again. This shows that as age increases people are more likely to get an illness that is treated at one of the departments. It has implications for mobility as well, because there are more old patients, this increases the probability of patients having an impaired mobility. These implications for mobility should be taken into account in the new design because mobility of the patients is an input for the new design.

Figure 4: Age distribution

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5. System analysis

The system analysis will investigate the differences between the departments in availability of lab test results during consults and the patient composition per department. Then there will be an investigation in the differences between the current design and the envisioned design. These investigations will be the basis of the requirements for the new design, which will be given at the end of this chapter.

5.1 Unavailability of lab test results during consults

This subchapter will analyze the differences between departments in available lab test results during consults, lab test processing time range, and the distribution of the processing times. From the system description is has become clear that the small time window between dropping off the specimen and the consult, leaves the Laboratory unable to process most lab tests in time before the consults. Therefore the availability of lab test results during consults is low for most departments as can be seen in Table 3. Two departments stand out in terms of available lab test results during consults, the Transplantation department because the availability is extremely low and the Nephrology department where the availability is substantially higher. The nephrology department has by far the highest availability, this has twofold reasons, firstly the necessary lab research for an average consult has a short processing time <2 hours, which enables the Laboratory to process the lab tests on the same day as the specimen has been dropped off. Secondly the physicians are familiar with the patients because these are recurring patients and are therefore more willing to drop-off specimen sooner to help the physician. For the Transplantation department on the other hand there are long processing times 9.5 hours for an average consult, this leaves the Laboratory unable to process the specimen before the consult takes place. On top of that the patients live at an average distance of 69 km from the hospital. Therefore it is not reasonable to let patients drop off specimen a day before the consult which therefore very rarely happens (1%). This indicates that the distance patients live from the hospital and the longest processing time have a major impact on the availability of lab test results during consults.

The Endocrinology and Diabetes departments have similar availabilities of lab test results during consults. The reason for this is that the secretaries of the Diabetes and Endocrinology departments fanatically call patients to drop off specimen in time before the consults. The Internal Medicine department treats patients with divergent illnesses which require different lab tests, around 35% of these consults just has a processing time of a few hours, and therefore the availability is 30% because most patients drop off the specimen a few hours before the consult.

Department Lab test results available during consults in percentages

Longest lab test processing time for average consult Lab test processing time range

Nephrology 65% 1-2 hours 1 hour – 1

day

Endocrinology 25% 1 week 1 hour – 3

weeks

Diabetes 24% 1 day 1 hour – 2

weeks Internal

Medicine 30 % 1 week 1 hour – 2 weeks

Transplantation 1% 9.5 hours 1 hour –

9.5 hours

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22 The analysis of available lab test results during consults in combination with processing times has shown that the clinical variability is substantial between departments. The analysis also revealed the causes for these differences and indicates that the time window the Laboratory needs has major impact on the availability of lab test results during consults. The Nephrology departments shows this by having a high availability of lab test results and also being the only department with short processing times for an average consult.

5.2 Solutions

This subchapter analyses three possible solutions for increasing the availability of lab test results during consults and reveals which solution will be investigated further and why. The master thesis of Jong (2016) investigated the efficiency of the Transplantation department and suggested three opportunities to improve efficiency. These included the renewal of Laboratory equipment, more frequent Laboratory assessments, and collaboration with regional BSUs. These opportunities apply to all departments because they all have the same basic process and problem, that lab test results are unavailable during consults and that this causes follow-up work.

The renewal of Laboratory equipment has already taken place, however the Laboratory is still not able to process a significant amount of lab test before the consults take place. Still, for some tests it is technologically possible to decrease the processing time by using Point of care technology, but this would increase the cost per lab test significantly. The second option of increasing the frequency of Laboratory assessments, is related to lab tests that have a batching process. The frequency of lab test assessments that follow a batching process could be increased by decreasing the batch size, however smaller batch sizes would increase the cost per test significantly. Because the first and the second opportunity increase the costs per lab test significantly, these opportunities are not economically viable. The manager of the Laboratory agrees with this and cannot implement these solutions because they are too expensive. The first two options are not feasible economically and the Laboratory equipment that was replaced did not have the desired effects on efficiency. The last option has not been investigated yet but shows the most potential, a 50% reduction of follow-up work (Jong, 2016). However, the collaboration with regional BSUs is the most far-reaching solution because it changes the current process of delivering care at the IMD. This is why the other two options were explored first.

The collaboration with regional BSUs is explored in this research by investigating if local BSUs of Certe could be used to increase the availability of lab test results during consults. This impacts the process of delivering healthcare, because it implies that the dropping of specimen will be done at a local BSU instead of at the UMCG BSU for a substantial amount of patients. The other change will be that dropping off specimen and the consult will not take place on the same day anymore. Because the time window between dropping off specimen and having the consult needs to be increased, thereby giving the Laboratory sufficient time to process the lab tests and publish the results before the consult.

5.3 New process of healthcare delivery

This subchapter investigates the new design by examining the differences between the current care process and the new care process. This will show the impact of the new care process on the patients and the departments involved.

5.3.1 Physician

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23 for most consults the lab test results will not be available. Therefore physicians try to invest as little time as possible in the preparation of the consult because this will not decrease the amount of follow-up work. However in the new situation where most lab test results will be available during consults it is worth investing in the preparation of the consult, this will abolish the follow-up work when done properly. Because when all critical information is available, the physician can make a diagnoses and treatment plan during the consult and communicate it properly to the patient during the consult as well. This completes the consult and does not leave any follow-up work. For the different departments the net savings have been calculated, these are shown in Table 4. The net savings are calculated as follows: (Situation lab results unavailable (preparation consult + follow-up work)) – (Situation lab results available (preparation consult + follow-up work)).

For the Diabetes department most consults are very similar and the diagnoses and treatment plans are simple, therefore the physician can process the diagnoses and the treatment plan quicker, this shortens the time needed for follow-up work and thereby the net savings per consult. The other departments have more complex diagnoses and treatment plans, this is why the savings per consult are higher for these departments.

Department Net savings

per consult in minutes Diabetes 7,5 Endocrinology 10 Internal Medicine 10 Nephrology 10 Transplantation 10

Table 4: Net savings in minutes per consult per department

5.3.2 Patient

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24 processing the lab tests in time. Thus when a patient effectively plans the two tasks a lot of wasted waiting time can be prevented. On top of that will the patient have a higher quality and complete consult, which means a better treatment and no time will be lost on calling with the physician after the consult.

The mobility of patients has important implications for the new design because patients that are less mobile cannot be burdened with two trips for reasons explained before. This means that the new design should be able to decide based on patient mobility if the patient has to make one or two trips. The distinction between a mobile patient and an immobile patient is that an immobile patient has to acquire help to get to the hospital (like a family member driving them), whereas a mobile patient can travel independently to the UMCG.

5.3.3 Laboratory

For the Laboratory there are two important aspects that need to be taken into account for taking in specimen locally. For some lab tests the processing of the specimen has to start within 4 hours of withdrawal from the patient to get accurate results. This mainly applies to lab tests on blood, and will not be as big a problem for urine, feaces and saliva. Specimen collected at local BSUs has to be transported to the UMCG Laboratory, this decreases the time window for collecting specimen at the local BSUs. This results in the following time window to collect the specimen at a local BSU: (4 hours - transport time to UMCG - safety buffer). Because the transporting time has to be taken into account if one wants to start processing the specimen within 4 hours of collection. The safety buffer is a small time buffer for unforeseen circumstances. When the transporting time is an hour the safety buffer could be 10 minutes for example for when one encounters slow traffic

Another thing that should be taken into account is that a big batch of specimen will arrive from the local BSUs instead of a steady flow from the UMCG BSU. When there will be made use of local BSUs the Laboratory should be able to process these big batches without increasing the processing times significantly. There should also be made a route through the UMCG to transport these batches of specimen efficiently and safely to the Laboratory.

5.3.4 Blood Sampling Unit

For the UMCG blood sampling unit nothing will change, except that they will experience a small decrease in specimen being dropped off because this part is now being dropped off at local BSUs. The local BSUs should be equipped with a printer and a computer that has access to the EHR of the UMCG. This will enable the employees of the local BSUs to open the orders for specimen collection and to print the labels that have to be put on the container that holds the specimen.

5.4 Findings

This subchapter will summarize the main findings of the analysis and translate them into requirements for the new design.

Firstly, it has become clear that the UMCG values patient friendliness at that this can be translated to the requirement that the new design has to take patient convenience into account.

The main requirement being that the patient convenience cannot decrease unreasonably by burdening the patient excessively.

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25 direct impact on the requirement of patient convenience, therefore a system is needed that can decide when the patient convenience decreases unreasonably for a patient, to decide if it complies with the requirement of patient convenience. Based on this decision the system should then plan the patient at the hand of the old or new care process. This leads to the following requirement; the new design should be able to plan the patients at the hand of their

mobility, the distance they live from the hospital and the processing times of lab tests needed.

In subchapter 5.3.2 it has become clear that both the mobility and distance patients live from the hospital influence the patient convenience, therefore they are included in this requirement. The processing times are also included in this requirement because these are critical to getting the specimen in time to the Laboratory for reasons explained before.

Thirdly, local BSUs should be incorporated into the new design because of two reasons, local BSUs have great potential for increasing the availability of lab test results during consults and they have a positive effect on patient convenience for patients that are planned according to new care process. Because patients that are planned according to the new care process have to make two trips, which decreases the convenience. But for the action off dropping of the specimen the travelling distance can be decreased significantly, which then increases the patient convenience. For example when a patient lives 70 km from the UMCG and 20 km from a hypothetical BSU, instead of having to travel 2 * 70 = 140 km for dropping of specimen at the UMCG, the patient now has to travel 2*20 = 40 km to drop off specimen, which saves him 100 km travelling distance and thereby increases the patient convenience. This leads to the following requirement; local BSUs have to be incorporated into the new design.

Fourthly, with regards to some lab tests - specimen has to be analyzed within four hours once they have been collected, because they have a limited durability for doing analysis on them. This leaves a limited amount of time to collect and transport the specimen towards the UMCG. This leads to the following requirement; the local BSUs should be localized within an hour

transporting time of the UMCG.

Finally, the local BSUs should be equipped with tools to collect specimen, with the main

requirement being the capacity for the extraction of blood from patients. There should also

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6. Design

This chapter will introduce a new design that includes two local BSUs and the UMCG BSU. With this new design the process of healthcare delivery has to be changed, although this does not apply to every patient. Therefore, a framework is created to distinguish between patients that are eligible for the new design and the ones that are not. It will be shown how the artefact that was created, transforms zip codes, age, and the critical processing time into a plan for dropping off specimen at the most appropriate BSU, and the moment when the consult takes place.

6.1 Planning framework

In this subchapter the framework for categorizing, planning, and determining the specimen drop off location for patients that has been developed, will be explained by discussing every step and every determinant of the framework.

Step one is to determine the mobility of a patient, which will show if the patient is mobile enough for making two trips. The determinants for this step are the following; the mental mobility, the physical mobility and financial mobility, in practice this will be determined by the physician who will estimate if it is reasonable to let the patient make two trips with regards to mobility.

Step two is to determine which BSU is closest to where the patient lives. If the closest BSU is within a 30 km radius than the patient is considered suitable for making two trips. The determinants for this are the distance between the residence of the patient and the BSUs and a function that determines the closest BSU. This is done by using a script that determines the haversine distances, by comparing the patient zip codes with the BSU locations and then determines the closest BSU, this script can be found in Appendix I. The haversine distance is used because the distance between two places does not change and is therefore steady measure. However, other measures could be used as well, like the travelling time, but this is harder to determine and might differ per day.

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27 Step five is where the patients arrive that are selected to make one trip because of an impaired mobility, a long traveling distance, or a short processing time in combination with a short travelling distance. The latter group of patients is selected to make just one trip because this is more convenient while it does not impact efficiency in a negative way. The other patients are planned to make just one trip because it is too inconvenient for them to make two trips. For the patients that have a short travelling distance in combination with a short processing time, the determinants are the same as for step four. However it should be noted, that these patients should drop off their specimen before a certain time instead of a certain day. This should also be taken into account when the Medical Administration plans the consult. There should be enough time between dropping off specimen and the consult, with the main constraint being the opening hours of the UMCG BSU. For the other patients the inputs are instructions from the physician and the date and time the consult is planned. Other inputs are not necessary because the lab test results will not be published in time before the consult.

Figure 5: Planning framework

Step 1:Determine the Mobility of the patient

Patient mobility sufficient for two trips?

Yes

Step 2:Determine which Blood Sampling Unit is closest to patient residence

BSU within 30 km of the patient residence?

Step 3:Determine the lab test with the longest processing time

Processing time longer than 2 hours?

Step 4: Create instructions for the patient, which include drop off date and location for

the specimen and the date of the consult

Step 5: Create instructions for the patient, for dropping of specimen and the going to the consult on the same day in the UMCG

Framework input (Patient charateristics)

Framework output

Patient instructions for healthcare service with

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6.2 New design

This subchapter will show how the new planning framework is applied to a case with two local BSUs in Odoorn and Heerenveen and clarify how these local BSU locations have been selected. It should be noted that the local BSUs used in the case represent real-life BSUs from Certe that have the capability to collect specimen. Certe is a company that provides integral medical diagnostics and advice, and is active in the provinces Groningen, Drenthe, Overijssel and Friesland. First a selection was made of BSU locations that had the capability to collect all types of specimen. This led to a selection of 123 BSUs of Certe that could be used, which are depicted in Figure 6. All these locations are within an hour drive from the UMCG, therefore they also fit the requirement of the transport time.

The goal for selecting the local BSUs was getting the highest patient coverage. This meant that as many patients as possible had to be in a radius of 30 km of one of the BSUs. However there should be a balance between overlap and coverage. A 30 km radius is used because the physicians of the IMD regarded 30 km distance as a reasonable distance for an extra trip to drop off specimen. Firstly, there was a circle with a 30 km radius put around the UMCG BSU which is also incorporated in the case. Then, two other circles with a 30 km radius were put on the map and visually placed so that the largest area was covered with the smallest amount of overlap. Thereafter, the center of the radius circles was compared with locations of Certe BSUs. This resulted in selecting the Heerenveen BSU because it covered a large area with little overlap. Ideally this BSU should have been placed at Grou, but unfortunately there is no Certe BSU around Grou. The other BSU in Odoorn was selected as far away from the UMCG as possible to decrease overlap and get as many patients as possible from the lower provinces.

Figure 6: BSUs of Certe capable of collecting blood

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Figure 7: Blood sampling units’ patient within 30km and 15km radius

Table 5 provides an overview of the number of patients within a certain range of a certain BSU. There is no overlap within a 20 km radius and a very small number of overlapping patients within a 25 km radius (159) meaning that 159 patients are in range within a 25 km radius of two BSUs. The 30 km radius has a significant amount of overlap. This is caused by the larger area of overlap, but also more specifically because the city Assen - which is quite big - falls within the 30 km radius of the Odoorn and the UMCG BSU. There are a lot of patients that live in the city of Groningen because there are 12570 consults related to patients that lived within a 5 km radius of the UMCG BSU.

Distance from the BSU

up to and including in km 5 10 15 20 25 30 UMCG 12570 15286 20330 24217 29772 33083 Odoorn 121 1053 2242 3896 5518 10200 Heerenveen 523 846 1478 2930 4131 6273 Combined BSUs 13214 17185 24050 31043 39262 44775 Overlap 0 0 0 0 159 4781

Table 5: Amount of consults with patients that live within a certain radius per BSU

To validate the framework a model has been build that simulated an application of the new planning framework on a case with two local BSUs: one in Odoorn and one in Heerenveen. One year of consults have been used as input for the model.

6.3 Data preparation

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30 not fit for analysis. In the end 57845 consults were included in the analyses after cleaning up the dataset.

Then the distances the patients had to travel to the BSUs in Odoorn, Heerenveen, and the UMCG had to be determined. This was done using a Python script which can be found in Appendix I, that compared the coordinates of the BSUs with the zip-codes of the patients. This script then provided as output the haversine distance (in km’s) between zip-codes of the patients and the coordinates of the BSUs for all three BSUs. This data was then added to the dataset so that it is ready for analysis.

With this dataset Table 6 could be created by selecting patients based on the distance they lived from a BSU. The percentage of patient consults that had a patient that lived within a certain radius of one of the BSUs is shown. This not the same as the number of patients that live within a certain radius of a BSU. This is an important distinction to make because only 6 percent of the patients made one visit in the period analyzed.

Distance from the BSU

up to and including in km 5 10 15 20 25 30 UMCG 21,7% 26,4% 35,1% 41,9% 51,5% 57,2% Odoorn 0,2% 1,8% 3,9% 6,7% 9,5% 17,6% Heerenveen 0,9% 1,5% 2,6% 5,1% 7,1% 10,8% Combined BSUs 22,8% 29,7% 41,6% 53,7% 67,9% 77,4% Overlap 0% 0% 0% 0% 0,3% 8,3%

Table 6: Percentage consults with patients that live within a certain radius per BSU

6.4 Model steps

A model has been built in Microsoft Excel that can calculate per department the impact of the new design, which encompasses making use of local BSUs in Heerenveen and Odoorn, and planning patients with the new process of delivering care. The model will be discussed step by step.

Step 1: Determine threshold travelling distance from BSU with regards to convenience

The first step encompasses determining a threshold distance to distinguish between patients that are suitable for the new design and those who are not with regards to the travelling distance. In consultation with the physicians of the IMD this was determined at 30 km.

Step 2: Calculate the total amount of appointments

Then the number of appointments has to be calculated for the department to be analyzed. This is done by selecting all consults of a certain department and counting them.

Step 3: Calculate the amount within threshold distance of a BSU

With the information of step 1 and 2 the number of appointments can be calculated that fall within the threshold distance of a BSU. This is done by a formula that counts all consults that has a patient that lives within the threshold range of a BSU.

Step 4: Calculate the percentage coverage

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Step 5: Calculate the immobility discount

The immobility discount stands for the amount of people that are not suitable for the new design because of impairments to their mobility. The mobility of patients is incorporated in the model by making age groups with their respective immobility percentages which are depicted in Table 7. Physicians of the IMD gave estimates of these percentages and agreed on the age categories created. The 100% immobility of the first category might need some clarification, virtually all patients in this category go to the hospital with the support of their parents. And for a patient to be mobile, the patient has to be able to travel independently to the hospital. Using these categories the number of patients that were immobile per age category could be calculated.

This is done by selecting all the patients within the threshold distance of the BSU(s) used in the case, then counting all the patients for every age category (Table 7 first column) and multiplying this with the percentage that is immobile (Table 7 second column) and then adding up the totals per age category. This provides the number of patients that are not suitable for the new design because of mobility issues.

Age category Percentage immobile 0 - 18 100% 19-49 5% 50-69 10% 70-85 25% 86-100 70%

Table 7: Age categories and percentages immobile patients

Step 6: Calculate the number of available lab test results during consults in the new design

The number available of lab test results during consults is then calculated by detracting the immobility discount (step 5) from the number of patients within the threshold distance (step 3).

Step 7: Calculate the number of available lab test results during consults in the current design

The number of available lab test results during consults is then calculated by multiplying the total number of appointments (step 2) with the percentage of available lab test results during consults for the respective department which can be found in Table 3.

Step 8: Calculate the improvement of available lab test results during consults

Then the improvement of available lab test results during consults can be calculated by detracting the availability in the current design (step 7), from the availability in the new design (step 6).

Step 9: Determine the savings per consult in minutes

Then the savings in minutes per consult have to be determined for the respective department, these can be found in Table 4.

Step 10: Calculate the yearly savings in hours

Increasing the efficiency of an outpatient clinic A design science study at the UMCG