Conclusions

In this section we repeat all research questions and summarize the answers to these questions. Furthermore, we give the most important findings of this research.

1. What is the current OR planning process for elective and emergency surgeries, and what resources are used?

By using HOH’s internal report ‘Optimalisatie OK-planning’ (Groenveld, 2018) and applying the healthcare framework (Hans et al., 2012) on HOH, we identified HOH’s resource capacity planning on different hierarchical levels in Chapter 2. Besides that, we created a flowchart to map the OR planning process.

2. What is the OR performance of the current scheduling strategy?

Furthermore, we performed a data analysis in Chapter 2, in which we showed several overviews about HOH’s specialties, case mix, urgency levels of emergency patients, utilization, late start, early end, overtime, cancellations, booking rate and booking accuracy.

The average utilization is 69%. The inpatient ORs show an average utilization of 73%, the outpatient OR is 49% of the time utilized. The total amount of overtime is 33,603 minutes, which is 4% of the OR session capacity and 15 minutes on average per session. 251 elective patients, 2.4% of all elective patients, needed to be cancelled because of planning reasons. Besides these three KPIs, we selected service degree emergencies and access time electives as KPI.

3. What are suitable scheduling strategies for elective and emergency surgeries for HOH?

In Chapter 3, we identified the following scheduling strategies as intervention possibilities for our simulation experiments:

• Switching from a hybrid policy to a flexible policy: stop reserving capacity for emergency surgeries by using the emergency OR (Emergency OR).

• Using the request list consequently (Request List).

• Minimum booking rate for OR sessions (MBR).

72 4. What is the effect on OR performance for the suggested scheduling strategies?

In Chapter 5, we analyzed the results of the simulation study we performed in Chapter 4 to answer this question. First, we identified the following KPIs with corresponding weights:

• Utilization – 0.5

• Overtime – 0.1

• Cancellations – 0.2

• Service Degree Emergency (SDE) – 0.1

• Average Access Time (AAT) – 0.1

Subsequently, we determined the relative scores of all Scheduling Improvement experiments on the KPIs and calculated the overall score of the experiments. The results showed that experiments with no Emergency OR, a Request List, high MBR and Slack between 5% and 10% gives high OR performance.

Furthermore, we concluded that SDE is not a preferable KPI. We measure the SDE by counting the number of times the service degree for emergency patients is not met. However, the absolute differences between the experiments are very small and therefore the relative difference are large. Besides that, the scheduling algorithm for emergency patients is not very robust and thus coincidence is an important factor for meeting the service requirements. However, the sensitivity analysis showed that the results do not differ much when we exclude SDE as KPI.

Experiment 60 has the best performance and the following settings:

• Emergency OR – False

• Request List – True

• MBR – 50%

• Slack – 5%

Table 7.1 shows the improvement potential of the best experiment, Experiment 60, opposed to the baseline experiment per KPI.

Table 7.1: Mutation of KPIs best performing experiment opposed to the baseline experiment

KPI Decrease/increase Utilization 6.5%↑ Overtime 0.8%↑ Cancellations 19.7%↓ SDE 1↑ AAT 33.3%↓

Table 7.2 shows the individual effect of the input variables on the KPIs. We relate the effects on the KPIs to the booking rate. If the booking rate increases, the performance on utilization and AAT increase, and the performance on overtime, cancellations and SDE decrease.

73 Table 7.2: Input – output relations

Input/KPI Utilization Overtime Cancellations SDE AAT

Emergency OR -- ++ + ++ --

Request List + - 0 0 ++

MBR ++ - - - ++

Slack -- ++ ++ + --

Concluding, according to the results of the experiments: HOH should stop using the emergency OR, make more use of the request list, make use of slack and adopt a minimum booking rate, since this increases the OR performance.

5. How should HOH apply the best performing scheduling strategies in practice?

According to the results of the simulation study, HOH should close the emergency OR. HOH is familiar with this situation, therefore we do not expect much is needed to implement this intervention. Furthermore, the results showed that slack is needed to ensure that the amount of overtime and number of cancellations stay in control. Specialists should be convinced that their OR session time increases when the emergency OR closes and slack is implemented. A drawback of implementing slack is that OR employees could think they have more time for the same amount of surgeries. So, they need to be convinced that this extra time is meant for operating emergency patients.

HOH uses the request list not consequently, but should do this according to the results. The OR planner seems the person to perform the job of rescheduling the cancelled patients. For implementing minimal booking rate, we suggest an online application in which specialists can hand in their OR program. If specialists do not succeed in meeting the minimum booking rate or handing in their program in time, their session becomes available for other specialists to claim it. For implementing the request list and minimum booking rate, we propose to run pilots.

To realize the interventions, we suggested a step-by-step plan that contains the following main steps: 1. Create a roadmap together with the OR committee.

2. Involve specialists and OR personnel. 3. Develop the online application (MBR). 4. Evaluate the changes.

By answering all research questions, we succeeded in accomplishing the research goal.

Research goal:

Evaluate the current surgical scheduling process and design a scheduling strategy to improve the OR performance.

74