Conclusion benchmark

The different hospitals that were interviewed all had their own reasons for deciding what the optimal elective admission ward would be like. Each hospital had certain recommendations that they suggest Rijnstate should think about. These recommendations are merged and summarised in the points of attention presented in Table 24.

Table 24. Points of attention that were found in the interviews with the different hospitals

Level Points of attention

Strategic The location of the elective admission ward should be as close as possible to the operating theatre Keep the walking distances for both the patients and personnel as short as possible

Develop protocols for the intake of all different patient types that arrive at the elective admission ward

Make sure that there are more beds than strictly necessary, since some people cannot or would not like to sit on a chair for a long time

Make decisions about what should be mentioned during takeovers

Decide with the receiving (inpatient) ward which information they would like to receive about the patients

Tactical Decide what the opening hours of the elective admission ward will be and make the operation planning based on these opening hours

Look into the planning process of the patients, since some patients will go to the inpatient ward after their operation and this means that two beds have to be reserved. One on the elective admission ward and one on the inpatient ward

Work with Hotflo or other operating planning programs to spread the workload of patients during the day for the surgeons and the nurses

Look into the logistics of the luggage of the patients, since the luggage is not allowed at Rijnstate to go to the operating theatre. Decide whether a luggage storing room is necessary and who is responsible for the transport of the luggage.

Make decisions about whether a storage room for extra beds is necessary at the elective admission ward.

Decide how the bed logistics will be planned and who is responsible for which part of the bed logistic process

Make sure that there is enough personnel available for the peak of patients at the elective admission ward

Operational Look into the process for the patients that are operated on at 8:00 hours. Who will take the blood samples when necessary

Make sure that the patient is asked as soon as possible whether he had something to eat or drink since the day before

Decide how the patients will be planned. Plan the patients who need a lot of preparations or have complex operations later during the day

Make sure that the anaesthesiologists have enough infusions that they can practice, since the infusion will be placed at the elective admission ward, but the emergency patients will get their infusion at the holding

If necessary, distinguish between the admission times of patients based on their mobility or the types of anaesthesia that are performed

5.2 Simulation model

The results of the different scenarios that were performed with the simulation model will be discussed per scenario below based on the performance indicators that were mentioned in Subsection 4.3.8. The influence of the number of intake and medication verification rooms on the total number of beds necessary will be examined. For each of the scenarios the patients are expected to arrive 60 minutes before the planned preoperative proceedings time. To find out if the scenario would be possible, the lead-time is examined, which is the time the patient spends in the process before being fully prepared at the elective admission ward. If this lead-time is longer than 60 minutes this means that the patients should arrive earlier at the elective admission ward. However, in none of the scenarios a lead-time exceeding 60 minutes can be found. The number of 20 chairs that were tested with the simulation model is sufficient for each of the scenarios; therefore it is not mentioned in all of the scenarios again. The scenarios should have an average rejection rate lower than 0.10%, a maximum rejection rate equal to or lower than 5% and a percentage of patients that arrive too late for their proceedings at the elective admission ward equal to or lower than 0.50%. For each of the scenarios there will be looked what amount of beds are necessary to not exceed these thresholds.

5.2.1 Scenario 1

The results of the simulation model with the original assumptions of Rijnstate of three intake rooms and one room for the medication verification are presented below in Table 25. It can be seen that the percentage of patients that are rejected equals zero when 16 beds are present at the elective admission ward. However, the maximum percentage of patients that can be rejected equals 7.41%. This means that even though on average no patients are rejected it is possible with 16 beds to still reject more than seven per cent of the day treatment patients. The workgroup at Rijnstate have decided that when taking into account the limits that were set in Subsection 4.3.8, the results of the rest of the scenarios are only presenting the number of beds that are equal to or higher than 14 since this is the first number of beds that does not exceed any of the mentioned thresholds.

Table 25. Results of scenario 1 with the simulation model

Nr of beds % Of day treatment

patients rejected [max]

Number of day treatment patients rejected [max]

% Of patients for which proceedings at EAW started too late 10 2.78% [40.00%] 0.48 [8] 4.23% 11 1.18% [37.50%] 0.21 [6] 2.23% 12 0.52% [23.81%] 0.10 [5] 1.23% 13 0.20% [16.67%] 0.04 [4] 0.63% 14 0.03% [9.09%] 0.01 [2] 0.38% 15 0.03% [12.50%] 0.01 [2] 0.31% 16 0.00% [7.41%] 0.00 [2] 0.26% 17 0.00% [0.00%] 0.00 [0] 0.27% 18 0.00% [0.00%] 0.00 [0] 0.23% 19 0.00% [0.00%] 0.00 [0] 0.25% 20 0.00% [0.00%] 0.00 [0] 0.25% 5.2.2 Scenario 2

In scenario 2 the choice was made for three intake rooms and two rooms for the medication verification to see which effect this will have on the percentage of patients that arrive too late for their preoperative proceedings at the EAW. When looking at Table 26 it shows that the number of intake rooms has no influence on the percentage of patients that are rejected and the differences in this percentage are based on the random distribution of patients for each simulation run. It can, however, be seen that the percentage of patients that start their proceedings too late at the EAW, has decreased with 0.20% for 14 beds in comparison to scenario 1.

Table 26. Results of scenario 2 with the simulation model

Nr of beds % Of day treatment

patients rejected [max]

Number of day treatment patients rejected [max]

% Of patients for which proceedings at EAW started too late 14 0.04% [10.53%] 0.01 [2] 0.18% 15 0.01% [8.7%] 0.01 [2] 0.09% 16 0.01% [5.0%] 0.00 [1] 0.03% 17 0.00% [0.00%] 0.00 [0] 0.03% 18 0.00% [0.00%] 0.00 [0] 0.02% 19 0.00% [0.00%] 0.00 [0] 0.02% 20 0.00% [0.00%] 0.00 [0] 0.02% 5.2.3 Scenario 3

In the third scenario, the influence of the change in the number of medication verification rooms to two is looked at. There is also a decrease in the number of intake rooms to two. When comparing the results of Table 27 to the results of Scenario 1 and 2 it shows that a decrease of the number of intake rooms leads to an increase in the percentage of patients who are too late for their proceedings at the elective admission ward. The number of intake rooms, therefore, is the bottleneck in the lead-time of the elective admission ward. There is still only a small probability of rejection for the day treatment patients, but the percentage of patients that start too late at the EAW is higher than the set limit of 0.50%. This scenario would not be preferable in comparions to scenario 1 and 2.

Table 27. Results of scenario 3 with the simulation model

Nr of beds % Of day treatment

patients rejected [max]

Number of day treatment patients rejected [max]

% Of patients for which proceedings at EAW started too late 14 0.06% [21.05%] 0.01 [4] 1.08% 15 0.01% [10%] 0.00 [2] 1.08% 16 0.01% [5.0%] 0.00 [1] 1.08% 17 0.01% [5.26%] 0.01 [1] 1.05% 18 0.00% [0.00%] 0.00 [0] 0.93% 19 0.00% [0.00%] 0.00 [0] 1.00% 20 0.00% [0.00%] 0.00 [0] 0.94% 5.2.4 Scenario 4

The results of the fourth scenario are presented in Table 28. This table shows that it even though the number of intake rooms remains set at two and only a decrease to one in the number of medication verification rooms is looked into this has a negative effect on the arrival of patients at the beds of the elective admission ward. The percentages arriving too late exceed the limit even more than was the case in Scenario 3, which can be explained by the low number of intake rooms that were found to be the bottleneck in Scenario 3. This scenario, therefore, would not be preferable with the current arrival time of 60 minutes before the preoperative procedures.

Table 28. Results of scenario 4 with the simulation model

Nr of beds % Of day treatment

patients rejected [max]

Number of day treatment patients rejected [max]

% Of patients for which proceedings at EAW started too late 14 0.07% [18.18%] 0.02 [4] 1.30% 15 0.02% [18.18%] 0.01 [4] 1.28% 16 0.01% [5.88%] 0.00 [1] 1.17% 17 0.00% [0.00%] 0.00 [0] 1.14% 18 0.00% [0.00%] 0.00 [0] 1.24% 19 0.00% [0.00%] 0.00 [0] 1.22% 20 0.00% [0.00%] 0.00 [0] 1.02% 5.2.5 Scenario 5

In this last scenario for deciding the optimal number of intake and medication verification rooms the effect of four intake rooms and one room for medication verification is analysed. The outcome presented in Table 29 shows that even though there is an extra intake room, this does not influence the percentage of patients for which the

preoperative proceedings at the EAW are started after the planned time. There is a difference of approximately 0.02% in comparison to scenario 1, which is almost negligible. There is also no actual difference in the average of patients that are rejected at the elective admission ward.

Table 29. Results of scenario 5 with the simulation model

Nr of beds % Of day treatment

patients rejected [max]

Number of day treatment patients rejected [max]

% Of patients for which proceedings at EAW started too late 14 0.05% [20.83%] 0.01 (5) 0.43% 15 0.02% [9.52%] 0.00 (2) 0.31% 16 0.02% [9.52%] 0.00 (2) 0.31% 17 0.00% [8.70%] 0.00 (2) 0.21% 18 0.00% [0.00%] 0.00 (0) 0.23% 19 0.00% [0.00%] 0.00 (0) 0.23% 20 0.00% [0.00%] 0.00 (0) 0.23%