TIMELY CARE DELIVERY AND ITS INHIBITORS: A CASE STUDY WITHIN AN URGENT CARE SETTING

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TIMELY CARE DELIVERY AND ITS INHIBITORS: A

CASE STUDY WITHIN AN URGENT CARE SETTING

Master thesis BA Health

1st supervisor: Dr. J.T. van der Vaart 2nd supervisor: Dr. M.J. Land

Student name: Iris Plat Student number: S3121704 E-mail: i.l.plat@student.rug.nl

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ABSTRACT

The aim of this paper is to gain a deeper understanding of wat makes it so hard to deliver timely care in an urgent care setting. This study is based on a single case-study. Quantitative data is used for analyzing cycle times whereby interviews are used to explore inhibitors to timely care delivery. Based on the analysis, it is found that cycle time norms are hardly reached, and that multiple inhibitors appear to be of importance in this. Implications of this research are that the study identified more specific antecedents in affecting cycle times in an urgent care setting than the general factors already identified in the literature. Furthermore, managerially it offers a practical oversight of adjustments needed to improve cycle times. The study is limited to a single case study in a single urgent care setting. Future research should focus on a multiple case study setting to make results more generalizable.

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3.4 Data analysis………...………….………14 3.4.1 Quantitative analysis……….……….………14 3.4.2 Qualitative analysis……….……...………14 4. RESULTS………..14 4.1 Quantitative analysis………14 4.1.1 Referral phase………...………14 4.1.2 Diagnostic phase……….………15 4.1.3 Treatment phase……….……….16 4.2 Qualitative analysis………..17 4.2.1 Planning mechanisms………..………...…………17

4.2.1.1 Evaluation of different planning mechanisms………..………18

4.2.1.2 Main takeaways……….…………..……….21

4.2.2 Other antecedents in affecting timely care delivery………...…….……..22

4.2.2.1 Variability……….………..………..……….22

4.2.2.2 Collaboration and integration………...…….………24

4.2.2.3 Resources……….………...………….……..26

4.2.2.4 Performance management………..…………...……….27

4.2.2.5 IT………..……….………..………28

4.3 Conclusion………..……….29

5. DISCUSSION……….……….30

5.1 Comparison with previous literature……….……….. 30

5.2 Importance of an aggregate view……….………....32

6. CONCLUSION……….………..……32

6.1 Theoretical implications………..………32

6.2 Managerial implications……….…………..……..……….32

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1. INTRODUCTION

Healthcare environments are continually evolving. Going back to the 1990s, hospital settings became: less stable, more competitive, and rules changed (McKee et al., 2002) (Healy &McKee, 2002). This trend of changing settings continues nowadays, as hospitals increasingly have to deal with aging, comorbidity, and chronic conditions (Gabutti et al., 2017). While even more pressures like financial issues, increasing expectations for care-quality, a growing list of specializations, evolving innovation, and the rise of management importance next to physicians add to complexity (Lega & DePietro, 2005) (Villa et al., 2009). Concluding, many internal and external pressures exist which create a complex and continuously evolving healthcare environment. Within this challenging environment, providing timely care has proven to be complicated (Schut & Varkevisser, 2013) (NZG, 2017) (Mediquest, 2020). Whereby timely care provision is the delivery of care within maximum time norms set by governmental agencies or healthcare institutions themselves.

It is worth paying attention to this problem as providing timely care-access is often as important as ‘what’ care is provided (IHI, 2003). Especially care settings with high urgency levels require timely delivery of care as time is inherently related to health outcomes (Henselmans et al., 2010). In this paper, urgent care is defined as a setting whereby quick diagnosis is needed to start with treatment at short notice. Besides the importance of timely care delivery for health outcomes, it is also essential for advancing hospital productivity and for increasing patient satisfaction (Litvak, 2009) (Villa et al., 2009).

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to scarcity, and it only leads to rising expenditures (Hans et al., 2012). At the same time, efficiency and cost reduction are also of great importance.

To summarize, providing timely care within norms has proven to be challenging. At the same time, timely care delivery is especially essential for the improvement of health outcomes, but also for hospital productivity and increased patient satisfaction. Within the literature, many general factors affecting timely care delivery are already discussed. However, often this subject is illuminated from single perspectives and not from an aggregate view. Furthermore, the literature still lacks an oversight with specific inhibiting factors to timely care delivery in an urgent care setting.

This study aims to gain a deeper understanding of the inhibitors that make it hard to provide timely care delivery in an urgent care setting. Many papers are and have been published on the topic of waiting times (and its antecedents). This indicates that antecedents of waiting times are still a matter worth discussing. Furthermore, the theoretical contribution of this paper lies in the fact that little is known about inhibitors of timely care delivery in urgent and complex care settings. Furthermore, it is shown that time norms are still not always reached (NZG, 2017) (Mediquest, 2020), indicating that the problems underlying waiting times are not all addressed. From a managerial perspective, this research will help in solving a business problem. The hospital setting used as a case study in this paper has difficulties in adhering to time norms, while a growing number of incoming patients is expected at short notice. Therefore, identifying the inhibiting factors in the care process that makes it hard to align with the time norms makes it easier to guarantee timely care delivery and cope with more patients.

In conclusion, the main research question is as follows: ‘What makes it difficult to achieve timely care delivery in an urgent care setting?’ A single case study is used to answer this research question. Whereby both quantitative and qualitative analyses are performed to gain deeper insights into care delivery and its inhibitors.

In Chapter 2, the literature review will overview key concepts related to the study of antecedents to timely care delivery. Chapter 3 then explains the methodology used, while Chapter 4 displays the found results. In Chapter 5, this paper’s results are placed in the current literature, and Chapter 6 will end with a conclusion.

2. LITERATURE REVIEW

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to business performance, one has to deal with the lives of real people. To manage performance, obligatory regulations exist to which healthcare organizations should dedicate themselves. For instance, Dutch Treeknorms describe general maximum waiting times before which care should be offered. Furthermore, more disease-specific performance indicators exist. Whereby SONCOS provides waiting time indicators for oncology care specifically (SONCOS, 2020). Besides norms set by external (governmental) agencies, care institutions themselves can also implement even stricter norms regarding time. These examples of norms exist to give patients some reliability that care is offered within strict time limits. However, even though these performance indicators exist, it is still a challenge to deliver all care within limits, as explained in the introduction.

In conclusion, it occurs that queueing/waiting times are so extensive that a patients’ cycle time exceeds time norms set by external agencies or care institutions themselves. This means that there is no timely care delivery. It is therefore interesting to look at the inhibiting factor causing waiting times to be high. The rest of this literature oversight will review the general factors that are already known to influence waiting and cycle times.

2.1 Planning

One main factor in affecting the timely delivery of care is planning. Because this is where the matching of supply and demand takes place. The used planning mode should, for example, depend on the level of urgency. As with a low urgency level, scheduling can take place well in advance, while in more urgent situations, an appointment should quickly be made (Gupta & Denton, 2008). Hans et al. (2012) divided four managerial areas of planning. Consisting of medical, resource capacity, materials, and financial planning. Each of these planning areas can exist within different levels of the healthcare organization. Planning on a strategic level creates aggregate decisions in the direction of the organization’s mission, while planning on a tactical level is made on an intermediate-term (Hans et al., 2012). Lastly, planning on an operational level is performed at the actual patient level and is used for short-term decisions (Hans et al., 2012). Furthermore, different planning strategies might be used to cope with uncertainty and varying resources. White et al. (2011) conclude that scheduling short and low variance appointments first creates the lowest waiting time. Furthermore, Gupta & Denton (2008) introduce the idea of batching patients before a follow-up will be made or scheduling a combination of multiple appointments on the same day. Implementing such planning strategies could reduce the waiting time between treatments and thereby decrease cycle times.

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relevant planning information to centralizing a part of the planning within a supply chain. A lack of planning integration may cause inefficient planning and thereby create unnecessary waiting times between appointments.

2.2 Variability

Variability plays an important role in affecting the cycle time of patient groups. Rutherford et al. (2017) argue that a distinction exists between common cause variability and special cause variability, which are also named natural and artificial variability by Litvak et al. (2005). As the former is natural and can be expected, while the latter arises due to inconsistencies in the care setting and are not inherent to the natural process. An example of natural variability is routing variety. As the routing through the care pathway depends on the severity and stage of the illness and the progression of a patients’ situation (Bhattacharjee & Ray, 2014). Joosten et al. (2009) argue that how healthcare systems are designed by ourselves has a greater impact on patients than natural variabi1lity. This points to the idea that artificial variability can have a big impact on cycle times. A second partition of variability is explained by Hopp (2008), who clarifies that both variabilities in arrivals and processing exist. Variability in arrivals can exist due to patients’ decisions whether to show up, how scheduling is taken care of, and when referrals come through from external care settings (Hopp, 2008). Variability in processing does not lie on the patients’ side but within the care setting itself and can thus be prevented. Therefore, besides artificial variability, variability within both arrivals and processing can also affect waiting times.

Concluding: the effect of variability is that queues come to exist within a care setting. To cope with this, either time or capacity buffers may be an option (Hopp and Spearman, 2008) (Thürer et al., 2014). However, buffering with capacity is costly and not always possible due to the scarcity of resources and the fact that resources can often only be used once. Therefore, variability will often be buffered with time, which negatively affects cycle times.

2.3 Collaboration and integration

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departments in a multidisciplinary care setting is missing, it will be harder to help patients on time as the process is then seen as separate building blocks.

Collaboration can be seen as a stepping stone for integration as the latter requires the former but not necessarily the other way around (Boon et al., 2009). Flynn et al. (2010) define supply chain (SC) integration as the degree to which a manufacturer strategically collaborates with its supply chain partners and collaboratively manages inter-organization processes to achieve efficient flows. Adapting this to care processes, it can be said that integration is the degree to which different departments in a certain care path collaboratively manage patient flows. Going beyond the general definition of integration, Leuschner et al. (2013) classified integration into different dimensions: information integration, operational integration, and relational integration. Furthermore, integration can also be grouped into certain extents/scopes of integration. The first one being no integration, followed by functional integration and internal integration, and the last one being external integration (Drupsteen et al., 2013). The extent of integration is just as collaboration also essential for facilitating an efficient flow of patients through the care process. However, integration within a complex and urgent care setting is not thoroughly studied yet. Coordination or integration may, on the one hand, be of importance in complex SC settings (Leeftink et al., 2018) (Mutlu et al., 2015) (Gimenez et al., 2012). While on the other hand, a complex environment also makes it more difficult to integrate (Bruns, 2013). Therefore, the effect of integration on timely care delivery in a complex, urgent care environment is not concluded in literature.

2.4 Resources

To decrease waiting times, extra resources or capacity might be added. However, as already discussed under variability (2.2), buffering with capacity is not always possible due to scarcity or high costs. This scarcity and high costs also highlight the importance of decision making on resources. Especially in the case of shared resources, collaboration is important. Van Donk & van der Vaart (2005) define shared resources as ‘common capacity used for different SC’. Whereby this sharing may be a barrier to integrative planning (Van Donk & Van der Vaart, 2004). When resources are shared, correct agreements should be made about when the resource is available for which department. As the planning needs to take into account at which specific times shared resources are available to match supply and demand. Collaboration within healthcare networks and care paths are thus needed to make the best decisions on resource distribution at an aggregate level instead of on individual patient levels. In order to prevent long waiting times. This aligns with the idea of Rutherford (2017), who argues that if only parts of a system (care for individual patients) are made to operate as efficiently as possible, the performance of the system as a whole (total care for all patients) will be sub-optimized.

2.5 Performance management

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deliver all care within the norms. As already pointed out before, care providers often recognize the need to organize care around patient groups instead of around the clinical services offered (Lega & DePietro, 2005) (Drupsteen et al., 2016). However, there is still an existing division between clinical and non-clinical employees. While non-clinical employees often focus on individual patients and their well-being, non-clinical employees might focus more on the overall patient group (Maynard, 1994). However, when steering on norms and cycle times, it is important to have a patient group focus. An individual patient process focus might create inefficiencies and make it hard to deliver timely care on a patient group level.

Even when time norms are agreed on within a care setting, it might still be hard to reach this when performance management mechanisms are not in place. For instance, when regular feedback on the adherence to time norms is not available, it is also hard to know how you perform as a care department. A lack of these mechanisms can thus affect waiting times. Haggerty et al. (2003) identified types of continuity needed in an interdisciplinary setting to reach common goals. Informational continuity means that there is a process in place in which information is constantly shared between care providers. Furthermore, management continuity means that plans and care protocols are shared between providers in a continuous matter. Multidisciplinary team meetings (MDT) are an example of both information and management continuity, as they discuss the progress of a patient. Taylor (2010) discusses that MDTs indeed improve coordination of patient care, thereby making it easier to reach set performance indicators.

2.6 Conclusion

Within this literature review, multiple subjects that affect waiting and cycle times were discussed. The general subjects that came to the surface were the importance of planning, variability, collaboration and integration, resources, and performance management. Within these general subjects, correct planning mechanisms and planning integration are essential factors affecting cycle times. Furthermore, artificial variability and variability in arrivals and processing can also have an effect. Moreover, the extent of collaboration and integration, making agreements on (shared) resources at the patient group level, and the existence of performance management mechanisms also affect waiting times. This extensive range of factors, which also have reciprocal relationships between them, shows that it is complex to understand how to affect timely care delivery.

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9 Figure 1: a conceptual model

3. METHODOLOGY

This chapter discusses how the research of this study is conducted. First, the research design is introduced, and the case selection is explained. After this, a clarification of the data collection and the data analyses follow. Which will be performed to answer the stated research question.

3.1 Research design

This paper aims to determine the inhibitors of timely care delivery in a care setting with a high degree of urgency. An urgent care setting in this paper is defined as needing a quick diagnosis to start with treatment at short notice, as time is inherently related to health outcomes. A case study approach is taken to receive an answer to the research question. Voss et al. (2002) describe that a case study can be performed using multiple cases from the same organization or researching a particular issue in multiple organizations. Within this paper, a single urgent care setting is studied by looking at quantitative and qualitative data. Performing a case study can lead to novel insights (Eisenhardt, 1989) and breakthroughs due to the less constrained nature in which the research is conducted (Voss et al., 2002). An abductive research approach is taken in this paper as it looks at the interplay between existing theories and case study data. As a result, additional theoretical contributions or new insights might be revealed (Timmermans & Tavory, 2012).

3.2 Selected case

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There is a general growing tendency to shift complex care to tertiary care hospitals while transferring less complex care to secondary care hospitals (HartNet, 2020). This shift of patients is also expected in the care setting studied in this paper. Whereby through this shift, a growing amount of patients is expected at short notice. This uncertain future makes it even more important to focus on understanding the obstacles to timely care delivery.

3.2.1 Description of the breast cancer care path.

The patient process in the breast cancer care path exists of the four main phases displayed in Figure 2. Firstly, the GP refers a patient to the hospital. Whereby he or she gives a first indication on which the initial patient trajectory will be based. A patient can either enter flow 1,2, or 3. Whereby flow 1 and 2 patients encounter a shorter care path as they often face benign problems. Whereas patients within referral flow 3 frequently enter the treatment phase after diagnostics are finished. After treatment, follow-up from multiple departments can be offered.

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Within the selected hospital case, plans are in place to reduce flow 1,2 and 3 to only two different ones. In practice, this new referral flow 1 will exist of the current flows 1 and 2, and the new referral flow 2 will exist of the current flows 2 and 3. The main idea behind decreasing the number of possible inflows is to improve the patient experience and decrease delays. Currently, GPs have to determine to which of the flows a patient belongs. When it becomes apparent after some screening that a patient is in the wrong referral flow, the hospital has to contact the GP again and ask for a new referral before the treatment can continue. This led to a delay in patient treatments and caused valuable time to go into calling the GP’s.

3.2.2 Insight into the care path.

Before discussing the actual research results, it is essential to have a first insight into the patient data. This gives a first idea of the overall patient group volume and the three different care pathways. From December 2017 until November 2019, 2603 first intake appointments were registered., which were attended by 2547 patients. This number of first intake appointments exceeds the number of patients with a first intake because a patient can enter flow 1 or 2 but might need a referral to flow 3 when a higher level of malignancy is detected during diagnostics. Figure 3, shows how the 2603 first appointments are divided over the three different care pathways. The figure shows that most appointments are registered for care pathway 3, which exists for people who have a high risk of malignancy. Furthermore, by comparing the two time periods, it can be seen that in the second one, a larger amount of first-time appointments were made for all three care pathways. This shows a growing amount of incoming patients. The fact that flow 3 shows more first time appointments is caused by the simple reason that patients with a high chance of breast cancer will get a referral to the hospital sooner than patients with low malignancy. Furthermore, it can also be that in the second period, there were more first-time appointments as a result of incorrect first-time referencing by the GP, causing patients to have multiple intakes for the different care pathways.

Figure 3: Number of first-time appointments

3.3 Data collection

In this research, both primary and secondary data were used (Hox & Boeije, 2005). Secondary data was collected by identifying multiple sources on oncology time norms to create an overview of maximum

92 106 129 190 950 1136 0 200 400 600 800 1000 1200 Dec. 2017-Nov. 2018 Dec. 2018-Nov. 2019 Dec. 2017-Nov. 2018 Dec. 2018-Nov. 2019 Dec. 2017-Nov. 2018 Dec. 2018-Nov. 2019

FLOW 1 FLOW 2 FLOW 3

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cycle time indicators. Furthermore, primary data was collected by looking at quantitative case study data on cycle times in the breast cancer care path. Additionally, qualitative data was collected by conducting interviews with employees in this care path. This was done to get their insights on inhibitors to timely care delivery.

3.3.1 Secondary data: time norms on breast cancer care.

Performance management is of significant importance for every organization´s success, and thereby also for healthcare organizations. Whereby especially performance indicators on time are essential to regulate cycle times. Within the Netherlands, different time norms exist for healthcare settings. For oncology care in general, SONCOS norms are fundamental in specifying maximum waiting times. Other organizations such as Integral Cancer Center Netherland (IKNL), NABON, IGZ, and ‘roze lintje’ also offer guidelines for maximum cycle times in (breast) cancer care. Table 1 shows an overview of these time indicators (IKNL, 2016) (SONCOS, 2020). Whereby also time norms of the case hospital itself are listed. The term PA in this table stands for an appointment in which either a puncture or biopsy is performed. Many cycle time indicators are based on this PA appointment as after this appointment, a diagnosis can often be made, and the rest of the treatment can be outlined. When it is detected that the diagnosis is malignancy, there are two options. For example, a patient may need extra diagnostics to learn more about the tumor’s location. When this is not needed, a patient can continue with the treatment trajectory.

Table 1: Maximum cycle times in the breast cancer care path

Maximum cycle time indicators

Case hospital SONCOS Other (IKNL, NABON, IGZ, roze lintje)

REFERRAL 1) Time between the referral and the first outpatient visit

Flow 1: 3 days Flow 2: 1 day Flow 3: 1 day

1 week 90% within 5 days

DIAGNOSTICS 2) Time between the first outpatient visit and the end of diagnostics

3 weeks

3) Time between the first outpatient visit and the first diagnostic PA action

3 days

4) Time between the first diagnostic PA action and MRI

1 week 90% within 10 days

TREATMENT 5) Time between the first diagnostic PA action and neoadjuvant therapy

80% within 5 weeks

5 weeks 5 weeks and 90% within 4 weeks

6) Time between the first diagnostic PA action and OR

3 weeks 5 weeks 5 weeks

7) Time between the first diagnostic PA action and OR with direct reconstruction

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The time norms of Table 1 were used to assess quantitative data on waiting times of the case hospital. These time norms are also depicted and numbered in Figure 2. This oversight makes it possible to assess where time norms are not reached and where attention should be directed for improving the timely delivery of care.

3.3.2 Primary data: a quantitative analysis.

Firstly, a quantitative analysis was performed on archival data. This data was provided by the Integral Capacity Manager of the case hospital and anonymized before usage. The archival data consisted of two different documents with both agenda information from the breast center and financial information. These documents showed which care was provided to which patient and in which sequence. With the information from these two documents, it was possible to retrieve the dates on which particular care was offered. This enabled the possibility of calculating waiting times for parts of the care process listed in Table 1. With this information, it could then be concluded whether the case hospital’s time norms were met.

Interview Function Date Duration in

minutes

1 Medical secretary breast center 17-11-2020 65 2 Nurse care coordinator day treatment

oncology ward 1c

17-11-2020 45

3 Admission and surgery planner 24-11-2020 45

4 MRI planner 24-11-2020 45

5 Pathologist 24-11-2020 45

6 MDT secretary (Medical secretary tumour working group)

01-12-2020 40

7 Nurse care coordinator breast center 03-12-2020 50 (8) Care coordinator breast center Contacted for information and

feedback on multiple dates. Did not follow the interview protocol.

-

(9) Unit head breast center Contacted for information and feedback on multiple dates. Did not follow the interview protocol.

-

Table 2: interviewed subjects

3.3.3 Primary data: a qualitative analysis.

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14 3.4 Data analysis

3.4.1 Quantitative analysis.

Patient data on cycle times were taken from two different data sources. One data set with information from the breast center agenda and one financial data set, which both displayed the dates of patient activities. Of these two documents, irrelevant data was removed before any calculations were made. Furthermore, before the analysis, the case hospital was asked which information they used to calculate their adherence to the time norms displayed in Table 1. This information was used to calculate the adherence to the time norms within these two available data documents.

3.4.2 Qualitative analysis.

The interviews were held to gain a deeper understanding of the processes within the breast cancer care path, and they were held until data saturation was reached. Some of the interview questions were used to establish a deeper understanding of the current planning mechanisms and to construct a supply chain overview. Other interview questions were used to extend the literature findings on variability, collaboration and integration, resources, and performance management. Planning mechanism data resulting from the interviews were afterward transcribed into an explanatory effects matrix (EEM). This table displays the planning mechanisms found during the interviews. Furthermore, the table shows the corresponding quote and the interviewee who provided the quote. An EEM helps to discover and show possible causation. Furthermore, it is a first step in which mechanisms are displayed and on which further analysis can be conducted (Miles & Huberman, 1994).

4. RESULTS

4.1 Quantitative analysis

This section looks at the cycle times of the different phases of the care pathway and whether the case hospital currently delivers timely care. Delivering timely care is again about offering care within time norms set by external agencies or hospitals themselves. The data is analyzed and compared with the target cycle times of Table 1. This section aims to find out to what extent the hospital department manages to deliver timely care. First, the referral phase is discussed, after which the diagnostic and treatment phase will be covered. Together these phases describe the total care pathway. Within the figures that follow, different time frames were used. As data on waiting times were not always available for the same period.

4.1.1 Referral phase.

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norm of one week. Still, the data shows that in 2019 the overall waiting times went up compared to 2018.

Figure 4: Indicator 1 -Time in days between the referral and the first outpatient visit 4.1.2 Diagnostic phase.

The second norm is the time between the first outpatient visit and the completion of diagnostics. However, whether the case hospital adheres to the SONCOS norm of three weeks is hard to compute as there is no clear treatment that marks the end of this trajectory. Therefore this second indicator is excluded from the results oversight.

A third indicator measures the waiting time between the first outpatient visit and the first diagnostic PA action. A PA action implies either a puncture or biopsy, and after this action, the diagnosis and treatment plan can be made. Figure 5 displays that the median, average, and (almost all of) the 90th percentile of the appointments have a waiting time of less than the maximum three days. Besides this, the median waiting time is zero days, which means that often a puncture or biopsy takes place on the same day as the first outpatient visit. Three patients were removed from this analysis as they had a waiting time of >300 days for these two steps. After carefully studying these patients, it was noticed that some appointment information was probably lacking, and therefore it was preferable to exclude them from the analysis.

Figure 5: Indicator 3 - Time in days between the first outpatient visit and the first diagnostic PA action 0 3 6 9 12 15 2017 2018 2019 2017 2018 2019 2017 2018 2019

FLOW 1 (198 pat.) FLOW 2 (319 pat.) FLOW 3 (2094 pat.)

Time to first outpatient visit

Median Average 90th percentile

0 0 0 0 0 0 0 0 0 0 2 4 6 Q4 (22 pat.) Q1 (112 pat.) Q2 (116 pat.) Q3 (156 pat.) Q4 (119 pat.) Q1 (87 pat.) 2018 2019 2020

Time to first diagnostic PA action

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A fourth cycle time indicator is the time between the first PA action and MRI. This MRI is performed when additional diagnostics are needed to learn more about the tumor’s location and malignancy. The case hospital has a norm of one week for this, while other organizations set the norm at 90% of patients within ten days. Figure 6 shows that the norm of one week or 90% within ten days is never reached from October 2019 to March 2020.

Figure 6: Indicator 4 - Time in days between the first diagnostic PA action and MRI

4.1.3 Treatment phase.

The fifth cycle time indicator is the time between the first diagnostic PA action and neoadjuvant therapy. This care phase’s norms are a maximum cycle time of five weeks, or 80% of the patients helped within five weeks. The start of the neoadjuvant therapy is retrieved from the data by looking at the first time a patient has an appointment in nursing ward 1c, which is the nursing ward in which this therapy is offered. In Figure 7, the cycle times are shown for the timeframe of late 2018 to start 2020. As can be seen in the patient numbers, not many patients end up having neoadjuvant therapy. Furthermore, the median waiting time is below five weeks for all periods, while only half of the periods have their average waiting time below those five weeks. The 80th percentile waiting time is relatively high. However, a decreasing trend can be detected.

The sixth cycle time indicator is the time between the first diagnostic PA action and a patient’s first surgery. The norm set by the case hospital is three weeks for this, while SONCOS and other norms are more lenient and are set on five weeks. Figure 8 shows that the median waiting time is around or below the five week mark, while both the average waiting time and the 90% percentile are clearly above this five week mark for almost all data points. As this sixth indicator is often not reached, it reinforces the idea that a change in the organization of care is needed.

The seventh and last performance indicator is the time between the first diagnostic PA action and the first surgery of a patient in which a direct reconstruction occurs. However, the case hospital only performed this surgery on eight patients from the end of 2018 to the start of 2020. Therefore, information on this is left out of the analysis.

The quantitative analysis concludes that, except for indicator 3, the time norms of Table 1 are generally not met. This indicates that there are problems with the organization of care in the entire care

0 15 30 45 60 75 October (16 pat.) November (18 pat.) December (17 pat.) January (19 pat.) February (12 pat.) March (10 pat.) 2019 2020

Time to MRI

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path. Therefore, the rest of this paper should focus on the entire care path instead of one specific phase. The finding that norms are generally not met reinforces the importance of studying inhibitors to timely care delivery. Whereby timely care delivery is about delivering care within norms set either internally or externally. Based on these quantitative findings, the qualitative analysis is performed to learn more about the underlying factors causing delays in this care path. Appendix B shows more quantitative analyses on parts of the care path for which there were no specific time indicators.

Figure 7: Indicator 5 - Time in days between the first diagnostic PA action and neoadjuvant therapy

Figure 8: Indicator 6 - Time in days between the first diagnostic PA action and OR

4.2 Qualitative analysis

During the interviews, different questions were asked to increase the understanding of the breast cancer care path. As a result, critical antecedents to timely care delivery came to the surface. First, different planning mechanisms are discussed and analyzed on their effect on timely care delivery. This is followed by a discussion of other critical antecedents that can be related to the literature review.

4.2.1 Planning mechanisms.

First, different planning mechanisms were deducted from the interviews. This made it possible to make an overview of the breast cancer supply chain and depict how patient activities are planned. Figure 9

0 15 30 45 60 75 90 105 Q4 (6 pat.) Q1 (35 pat.) Q2 (32 pat.) Q3 (46 pat) Q4 (39 pat.) Q1 (20 pat.) 2018 2019 2020

Time to neoadjuvant therapy

Median Average 80th percentile

0 25 50 75 100 125 150 175 200 225 Q4 (10 pat.) Q1 (90 pat.) Q2 (117 pat.) Q3 (124 pat.) Q4 (96 pat.) Q1 (61 pat.) 2018 2019 2020

Time to OR

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shows an overview of this supply chain. Tables 3,4 and 5 in Appendix C display different planning mechanisms that resulted from the interviews. A division was made in patient planning mechanisms (hereafter referred to as PPM) and resource planning mechanisms (hereafter referred to as RPM). This division became clear from the interviews as some people described how patients were planned into activities (PPM), whereas other interviewees described how resources were planned (RPM). Some examples of planning mechanisms were already covered during the literature review. Examples of these are batching of patients or scheduling multiple appointments on the same day (Gupta & Denton, 2008). In the next section, the effect of planning mechanisms on timely care delivery will be discussed. This is done by placing the mechanisms in the context of the performed quantitative analysis under 4.1 or extra insights in Appendix B and by continuously addressing the supply chain oversight of Figure 9.

4.2.1.1 Evaluation of different planning mechanisms.

The first PPM found is combining appointments with a strict (number of days) interval. Whereby these appointments are not planned on the same day. This mechanism is only suitable if enough capacity can be freed to adhere to the interval and if the information on these two activities is shared between the responsible planners. An example of this first mechanism is the six day interval that is agreed on between the MRI and PA after MRI appointment(quote 2&3 Appendix C), which is also displayed in Figure 9. Whereby the MRI appointment is planned by the MRI planner of the radiology department, and the PA after MRI appointment is planned by the secretary of the breast center. The interviews revealed that there are, however, some problems with communication and with freeing up enough capacity. Whereby the communication problem makes the capacity problem even more significant. First of all, little direct communication exists between the MRI planner and the breast center secretary. The breast center secretary manually needs to check specific patient numbers to see if the MRI for these patients has already been planned as there is no notification of this between the planners. Furthermore, for both the MRI and PA after MRI appointment, there is limited capacity. Quote 18 in Appendix C shows that there is only one spot per day for a PA after MRI appointment at the breast center, while sometimes more is needed. Also, the norm that MRI access should be guaranteed within one week is generally never (Figure 6) even though the MRI planner argues that enough room is allocated for breast cancer patients (quote 14 Appendix C). As a result, the six day interval seems to be hard to reach. This can also be validated by the MRI planner, who argues that ‘usually three to four days are needed before MRI results are clear’. Furthermore, after the MRI results are clear, an MDT should first be held before the PA after MRI appointment can occur. Moreover, the average waiting time between an MDT and the PA after MRI appointment is around four days (Figure 14 Appendix B). These three/four days and four days together do not add up to the six day interval. Especially the lack of communication between the planners seems to be an inhibitor to combining the two appointments and delivering timely care.

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patient shows malignancy, extra diagnostics might be needed. In this case, a patient is not immediately discussed at an MDT as this is waited for until the extra diagnostic results are available. This can also be seen in Figure 9, which displays that the first MDT is sometimes delayed. As there are uncertainty and a lack of communication around the planned MRI date, the surgeon initially requests a spot for a future MDT, hoping that all extra diagnostics for the patient are available at that moment (quote 4&5 Appendix C). The inefficiency in this planning mechanism lies in the fact that results can already be available earlier than the planned MDT date. Whereas at that moment, earlier MDT’s might already be filled with other patients. If this happens, it causes a delay in the patient process.

Another planning mechanism used by the case hospital is parallel planning. For example, after the first diagnostics, it may already be clear that a neoadjuvant trajectory is needed. In this case, both the planning of the MRI appointment and the first conversation at the neoadjuvant department are simultaneously asked for (quote 6 Appendix C). Figure 9 shows that between the MRI appointment and the start of the neoadjuvant trajectory, usually an MDT and a PA after MRI appointment takes place. By planning the MRI and the neoadjuvant step in parallel, time is efficiently used compared to a situation in which every step is planned when the former has ended. A second parallel planning mechanism shows that when the end of the neoadjuvant trajectory is in sight, a notification from this department is sent to the breast center. The breast center’s nurse care coordinator can then already plan a conversation with the patient to discuss the surgery (quote 7 Appendix C). The fact that this notification is sent before the neoadjuvant trajectory has ended, means that the surgery consultation capacity can already be reserved for that patient. As a result, the first conversation can quickly follow the end of the neoadjuvant trajectory, which saves time. Lastly, when it becomes clear that a patient needs surgery, a provisional OR date is already planned for (quote 8 Appendix C). This OR date’s planning is also parallel planned with all other intermediate steps that still have to be performed, such as an MDT or additional consultations at the breast center. Whether a patient needs surgery can sometimes already be known after the results of a biopsy are clear. Figure 9 shows that many appointments can be held between the diagnosis and the start of the OR trajectory. By planning the start of the OR trajectory as soon as possible, time is efficiently used.

A fourth PPM is backward planning. This means that one starts planning preliminary activities with an already set end date in mind. Within the neoadjuvant department, the planner receives an order from the internist-oncologist that states an ultimatum before which a patient should start its treatment (quote 10 Appendix C). This mechanism can also be found in Figure 9. Before starting the treatment within the neoadjuvant department, an information consult and medication check should be held. However, it seems that these two appointments are not performed as soon as possible but are delayed while having the ultimatum in mind. This backward planning mechanism appears thus inefficient as for cycle times to decrease; appointments should be performed as soon as possible.

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At the first outpatient visit, a biopsy is taken, which is analyzed on possible malignancies by a pathologist. Within the pathology department, planning happens at a short notice. As when biopsies arrive, they should be prepared for analysis within a matter of hours. As breast center biopsies have a higher urgency for analysis than other biopsies, they are prioritized at every process by guaranteeing capacity. In practice, this means that for procedures such as: cutting the biopsy, coloring the biopsy, or putting it in a machine, breast center biopsies are on top of the pile (quote 11&12 Appendix C). Biopsies of other specialties have to wait. This planning mechanism seems to be in place to adhere to the three-day interval that is agreed on between the biopsy and the PA after biopsy appointment (Figure 9). Whereby this prioritization with guaranteeing capacity seems to be efficient in reducing cycle times. A second and third part within the supply chain where prioritization of breast cancer patients is used is within the OR department and the MRI’s planning. Breast cancer patients are prioritized as capacity is reserved for them to guarantee a particular cycle time (quote 13&14 Appendix C). However, prioritization only works when enough capacity is reserved. This does not appear to be the case as time norms for the MRI or surgery are generally not met (Figure 6&8). Variability may be one of the factors causing this difficulty in forecasting needed capacity. Therefore reserving capacity based on the average amount of incoming patients can be causing delays in the care process.

For the first outpatient visit, the breast center also makes use of reserved capacity. As for flows 1,2, and 3, different spots are reserved in the schedule to guarantee both time norms and the possibility to be seen by a doctor (quote 19 Appendix C). Again, prioritization only works when enough capacity is reserved. However, Figure 4 of the quantitative analysis displayed that the hospital time norm between the referral and the first outpatient visit is generally not met. The current amount of reserved capacity is thus not always enough to meet demand. Variability can again be one of the factors causing this difficulty in forecasting, making it essential to leave some flexibility in the schedule.

Lastly, providing demand information is a RPM that can decrease waiting times. Sharing information on the number of incoming patients at a group level has recently been implemented between the breast center and the OR department (quote 22, Appendix C). Having demand information at an early stage makes it easier to adjust capacity, thereby helping patients on time. However, as already mentioned, this demand information is only shared at the group level between the breast center and the OR and not between, for example, the breast center and the neoadjuvant department. This lack of information sharing may cause waiting times.

4.2.1.2 Main takeaways.

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inefficiencies, as it delays the planning of appointments to a later date than needed. Besides this, prioritization is used in planning breast cancer patients in some parts of the care path. However, the difficulty in reserving enough capacity makes it hard to really ‘prioritize’ the breast cancer patient and adhere to the time norms. Variability can be one of the contributing factors that causes this difficulty in forecasting capacity. Whereby reserving capacity based on the average amount of patients can cause delays in a highly variable care process. Lastly, sharing demand information is made little use of in the care path. Currently, this information is only shared between the breast center and the OR planner. Whereby this demand information makes it easier to adjust capacity and thereby help patients on time. The lack of communication on incoming patients between other care departments in the care path may cause waiting times.

Furthermore, combining the information received from the interviewees and the insights from the quantitative data also led to the conclusion that there is no integral planning system for the entire care path. Instead, planning is quite fragmented. Figure 9 clarifies that, indeed, within the supply chain, many different planning parties exist. Furthermore, no single person is responsible for steering these planners. The following examples can also consolidate that a coordinating function is missing:

Nurse care coordinator breast center: ‘We had a period in which it took too long before OR could be planned. We wrote this down for ourselves. However, I do not know if someone keeps track of time norms and planning in the entire care path. There probably is someone, but I do not know who that is’.

Nurse care coordinator day treatment oncology ward 1c: ‘There is no coordination between the planners in department 1c and planners from the breast center […] generally speaking we do not have contact with the breast center […] For our department, planning is centralized, but this is not the case for the entire breast cancer care path, I think. We are just a small part of the care path who performs its work for the patient’.

MDT secretary: ‘Regarding the planning of the MDT, we have contact with the doctor. If needed, also with a specialist and sometimes also with the nurse care coordinator of the breast center´.

4.2.2 Other antecedents in affecting timely care delivery.

Besides planning mechanisms, other factors are also analyzed on their effects on cycle times and care delivery within time norms. These other factors will be discussed next, whereby it becomes clear that besides the antecedents already discussed in the literature review, IT also appears to affect cycle times.

4.2.2.1 Variability.

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full adherence to the set schedule and to cross-departmental collaboration. On the one hand, natural variability plays a role in the case hospital:

Medical secretary breast center: ‘Last week we got 30 referrals from GPs within 1 minute, and we do not have room for that […] sometimes there are patients throughout the entire day, and other weeks you could scale down´.

MRI planner: ‘We have to go with the flow. We do not get any patient forecasts from the breast center. Also, not all patients have to get an MRI. This depends on the diagnosis of the patient´. Nurse care coordinator day treatment oncology ward 1c: ‘There are always peaks and throughs throughout the year. Somehow we always manage it’.

Admission and surgery planner: ‘Our work depends on the patient flow coming from the outpatient clinics. We do not have much insight into this. Not all patients need to go to OR, and it is varying which type of OR is needed depending on diagnosis’.

Nurse care coordinator breast center: ‘Sometimes a patient has a share in the delay. A while ago, a patient wanted to go for a 2nd_{and 3}rd_{opinion. So that gave a considerable delay which we} could not do anything about’.

Besides natural variability that cannot be influenced in any way, artificial variability also plays a role within the case hospital. Whereby artificial variability exists due to the systems that people develop themselves. One source of artificial variability comes from how appointments are planned. As could be seen in the analysis of planning mechanisms, different mechanisms were used in the care path, all of which had their own effect on cycle times. Further examples of sources of artificial variability that the interviews displayed were: availability of staff, an increasing demand of appointments after an MDT, a patient being ‘lost’ in the system, and the choice of a specialist to purposely wait:

Medical secretary breast center: ‘When a doctor or surgeon says that he or she has a consultation on a certain day which decreases his or her availability, we have to cancel all patient appointments for that time frame’.

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MRI planner: ‘A specialist has to send us an internal order so that we can add a patient to the MRI planning. It sometimes happens that a specialist makes an order and forgets to press ‘send’. […] We only discover this when someone calls us to ask when the patient is planned’. Pathologist: ‘When we get a biopsy, we could theoretically cut this in 1000 pieces, which we could each analyze. Moreover, we could also use different colors to detect malignancy. […] In theory, we could do all this pathologic research at the same time. However, that is a tradeoff you make as not every biopsy is malignant and needs all this research. I take the time to wait and do the research in sequence instead of simultaneously so that I can base the next step on the outcome of the former step. As all extra research costs extra money’.

As a response to either natural or artificial variability, two primary responses were found, which correspond to the ones found in the literature review. These are buffering with time or capacity. Whereby the example of the pathologist above is also an example of buffering with time, as the natural variability in malignancies leads to the choice of not doing all research in parallel. Other buffering examples are:

Capacity: Medical secretary breast center: ‘Our care coordinator sometimes contacts radiology to ask for extra laboratory technicians […] we created more ultrasound spots per day. This was possible because radiologists are willing to start earlier and stop later.’

Capacity: MRI planner: ‘We reserve places for breast center MRI’s. Sometimes this is not enough, and then you plan these patients on different spots.’

Time: MDT secretary: ‘Sometimes there are twenty patients on an MDT and only three on the next one. Then we try to move patients to a next MDT to make it more balanced.’

Although the different possible patient steps of Figure 9 are quite clear, it appears that in some parts of the care path, both natural and artificial variability do form a barrier towards forecasting needed capacity and creating a well-informed planning. Whereby natural variability cannot directly be decreased. However, by sharing patient forecasts throughout the entire care path, this natural variability can be better anticipated. Currently, these forecasts are not shared as appeared in the quotes under natural variability. On the other hand, artificial variability can directly be influenced to decrease the time needed to deliver care.

4.2.2.2 Collaboration and integration.

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Medical secretary breast center: ‘My daily work is influenced by almost everyone: nurse care coordinators, doctor, laboratory technician, radiologist.’

Admission and surgery planner: ‘We depend on all departments.’

Nurse care coordinator day treatment oncology ward 1c: ‘I influence and am influenced by the whole team.’

Because of this high dependency between and within departments, collaboration and integration are vital to oversee patient progress. Within the case hospital, collaboration especially appeared to be present in MDT’s and in the form of singular contacts between parties if needed. Furthermore, if collaboration occurred, it mostly happened between only two members of the care path. Thus, central management and coordination of the entire patient process and consecutive appointments are missing, which can create waiting times. An integrated planning function in hospitals can help alleviate this problem (Vissers & Beech, 2005) (Aronsson et al., 2011). Whereby this integrative planning means that (a part of) the planning of the supply chain is centralized (Van der Vaart & Van Donk, 2004). Some examples that show that there are still ample opportunities to increase collaboration and integration in the care path:

Nurse care coordinator day treatment oncology ward 1c: ‘I think that the internist-oncologist who refers patients from the breast center to our neoadjuvant department, has insight into the influx of new patients into the breast cancer care path. However, to what extent that is communicated to our department? Maybe this is discussed in the tactical planning consultation, but we do not hear anything about it. […] For the breast cancer care path, I do not think that planning is centralized. We are just a small part of the bigger picture that does its work for the patient.’

Nurse care coordinator breast center: ‘There is no information sharing between the breast center and, for example, the admission and surgery planner or department 1C on the influx of new patients. Not that I know of.’

Admission and surgery planner: ‘Our work depends on the patient flow coming from the outpatient clinics. We do not have much insight into this.’

MRI planner: ‘We do not get any patient forecasts from the breast center.’

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As can be concluded from the above statements, many opportunities exist to increase collaboration and integration within the care path. Whereby increased integration in a hospital can help alleviate waiting times (Vissers & Beech, 2005) (Aronsson et al., 2011). As collaboration and integration are currently not fully exploited, this inhibits timely care delivery.

4.2.2.3 Resources.

The availability and sharing of resources is a factor that adds to complexity. Within the breast cancer care path, there are some dedicated resources: such as the ultrasound, the mammography, and the nurse care coordinators that accompany a patient. Furthermore, there is reserved capacity of shared resources. In pathology and the MRI department, this is used in order to offer priority to breast cancer patients. Lastly, patients use non-reserved capacity of shared resources such as in the neoadjuvant department for chemotherapy treatment. The fact that not all resources are always available leads to problems in the timely delivery of care:

Medical secretary breast center: ‘There is only one place per day for a result (PA) after MRI appointment, as the surgeon only has one spot each day. And often, you also need an ultrasound spot for that patient on the same day. Sometimes we do have a spot at the surgeon, but there is no room to do an ultrasound.’

Nurse care coordinator breast center: ‘It is hard when a patient has breast cancer and can only be helped in two weeks at the MRI department […] we are of course not the only one making use of their MRI.’

The underlying causes of the unavailability of resources in the case hospital appear to be multiple. As already discussed under planning mechanisms, it is hard to reserve enough capacity within the planning due to the existence of variability. Besides this, not all information on patient flows is shared throughout the entire care path through which it is hard to adapt capacity. Furthermore, in the case of shared or scarce resources, it is essential that an aggregate view on patient flows is taken rather than an individual patient view. However, it appeared that in some cases, a rather individual view is taken:

Nurse care coordinator day treatment oncology ward 1c: ‘We focus on the individual patient process. Thereby we try to take the wish of the patient into account. For example, whether they want to come in the morning or afternoon or on which day. We do not regulate the patient process on a group level’.

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The information above concludes that the unavailability of (shared) resource sometimes cause a problem in the timely delivery of care. Due to an individual patient view instead of a more aggregate view on the patient group, inefficient decisions on resource distributions may be made. Rutherford (2017) already argued that if only parts of a system operate as efficiently as possible, the system’s performance as a whole will be sub-optimized. Furthermore, due to variability and a lack of communication on patient forecasts throughout the entire care path, it is hard to create a well-informed planning and adapt capacity. This leads to waiting times.

4.2.2.4 Performance management.

During the interviews, the employees mentioned that the time norms set by the hospital were quite clear. However, distinct norm steering mechanisms were missing:

Medical secretary breast center: ‘I think that waiting list information is shared. When, for example, the MRI planner sees that MRI waiting times increase, then their supervisor will give a signal to our supervisor, and our supervisor passes this on to us. We cannot see waiting list information ourselves. […] We do not get weekly feedback on time norms.’

Nurse care coordinator day treatment oncology ward 1c: ‘There is no overview in which we can see cycle times of a patient […] there is no quick overview of the entire care path.’

Nurse care coordinator breast center: ‘When there were time norm problems with the OR department, we consulted them. We made a special workgroup for this problem.’

Admission and surgery planner: ‘Feedback on time norms is not often given to me. We just look at the first spot where we can plan a patient for surgery. […] Within the internal order from the surgeon, it states the urgency and the last possible admission date. However, we do not do that much with the urgency. Often we just plan the patient at the first available spot.’

Furthermore, the norm steering responsibility lies with many different people. Whereby none of them seems to have an overall view of the care path:

MRI planner: ‘No specific person is responsible for monitoring time norms within radiology. […] We do it together: both signaling waiting times and steering on norms.’

Nurse care coordinator breast center: ‘I do not know where the responsibility for tracking time norms lies. There probably is a responsible person, but I do not know who.’

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It appears that mechanisms to steer on norms and the responsibility for this are not communicated and divided. Therefore, even though the time norms are explicit, it is hard to check real-time adherence. This compartmentalized performance management is a result of the patient-focused culture, which was elaborated on under 4.2.2.3.

4.2.2.5 IT.

IT appeared a crucial factor in affecting timely care delivery, which was not covered in the literature review. This factor can thus be added to the conceptual model. Within the interviews, it became apparent that IT, on the one hand, positively influences cycle times as it shows possible appointment time slots to the planners. On the other hand, IT can also be a bottleneck in planning as it appeared that notifications did not always come through. Furthermore, used IT systems were not always well-equipped, making it hard to keep track of patients’ progress. This results in the fact that keeping track of patients is often still done in an old-fashioned booklet instead of in an automated system:

MRI planner: ‘A specialist needs to send us an internal order so that we can add a patient to the MRI planning. It sometimes happens that a specialist makes an order and forgets to press ‘send’. This happens regularly, and we only discover this when someone calls us to ask when the patient is planned. This button can apparently not be removed. It was added to the system last year, and beforehand we did not have this problem.’

Nurse care coordinator breast center: ‘We do not get an automatic notification when a patient is planned for an MRI. It is quite a lot of work to keep track of everything for everyone. We keep track of everyone manually.’

Nurse care coordinator day treatment oncology ward 1c: ‘Within our online patient program, there is no overview of patient cycle times. We can, of course, look into an individual patient’s online dossier. However, there is not a quick overview of the entire care path.

Medical secretary breast center: ‘I keep track of every patient’s progress by looking at my booklet with patient numbers that I still have to check […] We make a daily word document in which we keep track of how many patients are in our schedule in the morning. At the end of the day, we write down how many patients we planned during the day. We mail this word document to our unit head or the care coordinator.’

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The quantitative and qualitative analyses show that some improvements in timely care delivery can be made. Firstly, 4.1 has shown that time norms in the case study are generally not met. Furthermore, this appeared to be valid throughout the entire care path. This indicates that problems exist in the organization of care. As time norms were generally not met, the qualitative analysis could shed light on specific indicators that inhibit the timely delivery of care.

The qualitative research uncovered many important inhibitors to the provision of timely care. Some of these factors were already identified during the literature review (except for IT), however, the qualitative analysis allowed for a more specific depiction of inhibitors. Figure 10 shows a revised model based on the findings of this research. Whereby these inhibitors combined affect timely care delivery.

A first qualitative finding was that different planning mechanisms were used to plan patients and resources. Whereby each planning mechanism had a distinct effect on cycle times. Found examples of inhibitors to timely care delivery in planning were: a general lack of communication between planners, purposely delaying appointments due to backward planning, and difficulty in prioritizing patients and reserving enough capacity due to variability and a lack of insight in this. Furthermore, planning is quite fragmented, and an integrated planning system for the entire supply chain is missing. Moreover, the supply chain misses a single person steering the multiple planners.

Secondly, variability appeared of importance in affecting cycle times within the case hospital. While natural variability cannot directly be influenced, sharing patient forecasts creates better anticipation of this variability. Furthermore, examples of artificial variability that can be addressed to reduce cycle times were: the availability of staff, a peak in demand for appointments after an MDT, patients being lost in the system, and the choice of a specialist to purposely wait.

Also, central management and coordination of the entire patient process and consecutive appointments are missing. This creates waiting times, which can be alleviated by an integrated planning function, as was already mentioned. The lack of collaboration comes in the form of little information sharing between departments and a rather compartmentalized focus.

Furthermore, the unavailability of (shared) resources arose as a problem in the timely delivery of care. The underlying factors causing the unavailability of resources appear to be a lack of insight into variability and thereby an inability to adapt capacity, and a rather individual patient view. This individual patient view creates inefficient decisions on resource distributions. Together these factors inhibit timely care delivery.

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Lastly, IT appeared as a crucial factor in affecting cycle times. The inability to work with the current IT systems and unwell equipped IT systems in general are the most important inhibitors to timely care delivery. Furthermore, an easy real-time overview of patient processes is missing. An outcome of this is that the patient process is often manually tracked, which is a precursor of mistakes that can lead to delays.

Figure 10: revised model: inhibitors of timely care delivery in an urgent care setting

5. DISCUSSION

This section will discuss this paper´s results in light of the already existing literature. First, the six main groups of inhibiting factors will be discussed, followed by a discussion of the importance of an aggregate view on the inhibiting factors.

5.1 Comparison with previous literature

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lacking. These inhibitors to timely care delivery are in line with the idea of Vissers & Beech (2005) and Aronsson et al. (2011), who argue that an integrated planning function in hospitals can decrease waiting times and that a lack of integration may thus increase waiting times. Moreover, it was found that the lack of communication between the different departments on the number of incoming patients in the care path made it hard to reserve enough capacity at all times, thereby creating waiting times.

Furthermore, both the lack of anticipation on natural variability and the existence of artificial variability appeared to be an inhibitor to timely care delivery. This is somewhat in line with earlier findings of Joosten et al. (2009), who argue that the way we design healthcare systems ourselves has a more significant impact on patients than natural variability. An additional finding of this research, is that for natural variability to have a minimal effect on cycle times, increased anticipation on this variability is needed to increase the flexibility in adjusting capacity.

Moreover, this research found that a departmental focus in the case setting still exists and that a lack of collaboration and integration could be found. This was mainly present in little communication between departments, a fragmented planning system, and a lack of central management. The fact that these factors were missing negatively affects the timely delivery of care. This is in line with the idea of Lega & DePietro (2005), who argue that hospitals need to shift away from a departmental focus to achieve common goals. Furthermore, the findings are also in line with the ideas of Mutlu et al. (2015) and Leeftink et al. (2018), who argue that SC integration can positively affect overall firm performance, whereby a lack of it might thus negatively affect performance. Besides, Brown et al. (2016) stressed the importance of a healthcare setting’s cohesive organization in creating a continuous knowledge flow between different departments. Within the breast cancer SC, this continuous knowledge flow was lacking. The literature review also discussed that integration is of importance in complex SC settings, while a complex environment makes it also more challenging to integrate. This research argues that even though integration might be hard to achieve in a complex environment, it is essential in achieving a more process-focused view.

The lack of a process-focused view could also be seen in the individual patient view that many departments in the breast cancer SC had. An individual rather than an aggregate view on patient flows may create inefficiencies in resource distribution and thereby inhibit timely care delivery. This aligns with the idea of Rutherford (2017), who argues that if only parts of a system (care for individual patients) are made to operate efficiently, the performance of the system as a whole (total care for all patients) will be sub-optimized. Furthermore, this research also found that again the lack of communication on incoming patient flows leads to an inability to adapt capacity, thereby inhibiting timely care delivery.

TIMELY CARE DELIVERY AND ITS INHIBITORS: A CASE STUDY WITHIN AN URGENT CARE SETTING