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The assessment of pooling intensive care and high care units at the neonatology department of Wilhelmina Kinderziekenhuis

Oude Weernink, A. (2018) The assessment of pooling intensive care and high care units at the neonatology department of Wilhelmina Kinderziekenhuis.

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Abstract:Background: In 2021, the renovation of the neonatology department of Wilhelmina Kinderziekenhuis will start. In the new lay-out, the current intensive care (IC) units and the high care (HC) unit are merged into two large spaces with 32 separate family rooms in total. The department has the choice to either preserve the division of 24 intensive care rooms versus 8 high care rooms, or change their capacity strategy by making all family rooms accessible for both patient types, which is called a total pooling strategy. To make an informed decision, the neonatology department wants to assess the in uence of such a strategy change on the number of rejections and the number of patients treated. Goal and method: In this report, we evaluate the current performance of the neonatology department by using three types of mathematical models. We want to validate whether these models are an adequate estimation of current processes and whether they can assess the in uence of merging the IC and HC units. Moreover, with the validated models we want to conclude whether merging care units will be beneficial for the performance of the neonatology department of Wilhelmina Kinderziekenhuis. We make an overview of current processes that are related to admitting, rejecting, transferring and discharging patients. Moreover, we determine the current performance of the department based on available data. The overall performance is defined by the number of patients treated and number of rejections, which are called the key performance indicators (KPI). Since these numbers depend on the available resources, an overview about used bed capacity is made as well. We evaluate the performance of the current lay-out by using three types of mathematical models: Erlang loss model, workload control systems and a simulation model. With the same models we assess the in uence of merging the neonatal units. Based on this information we conclude whether these models are good estimators for the performance of the neonatology department and whether merging the intensive and high care units will be beneficial. Context analysis: The neonatology department has a maximum capacity of 32 beds, of which 24 IC beds and 8 HC beds. Unfortunately, not all beds are open, since the number of IC arrivals. There is one patient population that has the highest probability of being rejected: the multiple births. In 2017, around 50% of all rejections were patients part of a multiple birth. This is caused by that patients part of a multiple birth all have to be admitted to a bed, or they are rejected when not enough beds are available. When a patient no longer needs the intensive care, the patient can be either transferred to the HC unit or to a peripheral hospital. 84% of the IC patients is transferred to a peripheral hospital, and 16% of all IC patients is transferred to the HC unit. Results: In this thesis, multiple types of results are generated. At first, results are given for assessing the effect of resource pooling at the neonatology department. Secondly, results of the comparison of the three models are given. With the Erlang loss model, the PAC model, and the simulation model, we generated results that showed that total resource pooling will be beneficial for the neonatology department. In case of the Erlang loss model, 24 IC beds result in a rejection probability of 15% for IC patient, and 8 HC beds result in a rejection probability of 13%. Both probabilities are higher than the rejection rate of 2016: 12.5%. However, when the resource are pooled into 32 beds and a combined arrival rate and service rate is calculated, we found a rejection probability of only 6%. By assessing the in uence of resource pooling with the PAC model, the waiting time for patient was eliminated. With using 28 beds the expected waiting time was 5.38 hours with the no-pooling strategy. In case of the total pooling strategy the expected waiting time is 0.00 hours. Afterwards, a simulation model was made and the performance of the department was assessed with this model. We conclude that the positive effects of resource pooling are larger with a smaller number of open beds. On average the number of rejections were decreased with 30%. Conclusion and recommendation: Based on the results found by analysing historical data, by performing calculations with the Erlang loss model and PAC model, and by simulating the department, we conclude that total resource pooling reduces the number of rejections and increases the number of admissions. Therefore, we suggest that the neonatology department of Wilhelmina Kinderziekenhuis should build 32 identical family rooms, which can treat both IC and HC patients. In this way, the neonatology department will be able to treat as many patients as possible with their limited capacity. Moreover, the �nancial bene�t of resource pooling is significant, since by pooling 18 IC beds and 6 HC into 24 universal beds, the income of the department increases with e1,588,651. We also have two types of recommendation: for the neonatology department and for further research. We recommend that the department should build 32 identical family rooms to make resource pooling possible. Moreover, we think that further research in applying workload control systems in healthcare could be beneficial. open beds is depending on the number of available nurses and the department currently has a shortage of personnel. In 2016, the department was operating with 20 beds on average. On average, every 11.75 hours an IC patient arrives at the neonatology department. This results in more than 600 patients arriving on a yearly basis. These patients are new-borns, and they are in need of acute and intensive care. Unfortunately, not all IC patients can be assigned to a bed at the neonatology department, since the number of available beds is limited. When no bed is available for the treatment of a new patient, the patient is rejected. In 2016, 93 patients were rejected, which is 12.5% of all
Item Type:Essay (Master)
Faculty:BMS: Behavioural, Management and Social Sciences
Subject:01 general works
Programme:Industrial Engineering and Management MSc (60029)
Link to this item:http://purl.utwente.nl/essays/74999
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