Staffing won't be worse with one fewer nurse: Improving the staffing of nurses in the Intensive Care Department of the Medisch Spectrum Twente

Goday Verdaguer, A (2017) Staffing won't be worse with one fewer nurse: Improving the staffing of nurses in the Intensive Care Department of the Medisch Spectrum Twente.

Abstract:In the framework of completing the master thesis of Health Sciences, I performed research at the Intensive Care Department (ICD) of the Medisch Spectrum Twente (MST) into studying how to improve the sta�ffing of nurses. In January 2016 the MST moved into a new building. With this change, the ICD was expanded and the capacity increased from 28 beds -in the past situation- to a maximum capacity of 42 beds. The units also changed. In the past situation there was one Thorax and one General unit, whereas in the new situation there is one Thorax unit, two General intensive care units and one Medium care unit. With the change of situation, the medical manager from the ICD thought about the possibility of adjusting staffi�ng to the activity, with the purpose of increasing the e�fficiency, because he had the impression that there were more nurses than needed. He also had the feeling that when there are more nurses than beds to cover the motivation of the nurses decreases. Until now, the methodology used for sta�ffing is based on the bed capacity. Thus, he wanted to study the possibility of switching to staffi�ng based on demand, instead of capacity. The objective of this research is to study how the current methodology employed for nurse staffi�ng purposes can be improved. Not only do we consider staffi�ng based on demand, but we also study other sta�ffing approaches. This leads to the following central research question: "In what way can the current capacity based staffi�ng be improved in the Intensive Care Department of the MST?" In order to find an appropriate approach for answering the research question, we have to take into account that the ICD underwent an important expansion. There was not enough data from the new situation to extract conclusions about possible changes. Moreover, the data was not reliable. Together with the manager, the following assumption was agreed upon: if an approach would have been effective in the old ICD, it will be effective in the new ICD as well. Accordingly, this is a retrospective study using data from 2012 to 2015. Before studying the staffi�ng approaches, we analyzed the past situation of the ICD with data from 2012 to 2015. The most relevant information found was that the most common bed occupancy rates were between 71% and 90%. In this project we consider three staffi�ng approaches: sta�ffing based on demand, based on capacity and based on a hybrid approach. Within each approach, we consider different scenarios. These approaches and scenarios are: (i) Demand approach: the demand approach consists in predicting the daily average number of beds occupied per week in 2015. The scenarios considered are: perfect prediction, naive forecast, moving averages and seasonal indexes. We also forecasted the demand using ARIMA models. Even though we found mathematically correct models, the prediction was close to the mean of the data and did not show variations. Since we were not satisfied with the results, and the application of ARIMA models is time-demanding and requires forecasting skills, we decided to develop heuristics for demand prediction. The heuristics developed are the aforementioned demand scenarios (except for the perfect prediction). (ii) Capacity approach: the capacity scenarios calculate the number of beds occupied based on a percentage of the maximum available capacity. The different scenarios considered are: 100%, 85%, 80%, 75% and 70% of capacity. For example, 85% of capacity means that we assume that 85% of the beds are occupied, even though 100% of the bed capacity is available. (iii) Hybrid approach: it is a combination of capacity and demand staffi�ng. First, staffi�ng is done based on a percentage of the maximum available capacity. Then, a reinforcement of this staffi�ng is done based on demand forecasting (using seasonal indexes). This means that the hybrid approach takes the maximum value between the capacity and the demand approach. The scenarios are: 85%, 80%, 75% and 70% hybrid. The presented scenarios were discussed from three perspectives: waste, financial and practical point of view. From a waste viewpoint, we were looking for the scenario with the best allocation of nurses, i.e., the least amount of extra hours and unnecessary hours worked. This resulted to be the 75% capacity scenario, with a pool of nurses of 61.68 FTE. This implies 20.6 FTE less than using the 100% capacity scenario. From a financial point of view, the 70% capacity scenario is the best one due to the reduced costs. Taking into account the pool of nurses and extra hours, the 70% capacity scenario adds up to a total amount of 1,844,296 e, whereas sta�ffing based on 100% capacity costs 2,617,600 e. This supposes a cost reduction of 29.5%. Nevertheless, due to the lack of nurses that this scenario implies, and that the resulting nurse-patient ratios differ significantly from the optimal-defined ones, the implementation of this scenario is not wise. So far, we have seen that the waste and financial viewpoints are not aligned. Moreover, we also have to take into account a practical perspective. In this project, reality has been simplified, thus, the scenarios mentioned so far might be too adjusted and not appropriate to be implemented. Therefore, taking into consideration the fact that changes are diffi�cult and that reality has been simplified, we consider that a scenario closer to 100% capacity might be a good option - such as staffi�ng based on 85% of capacity. Answering the research question: (i) Demand approach: we consider it too risky due to the amount of extra and unnecessary hours of work that it has. Moreover, a considerable amount of reliable past data is needed, which is often diffi�cult to obtain. (ii) Capacity approach: it is possible to improve the current staffi�ng methodology using this approach, by reducing the percentage of capacity based on which the sta�ffing is done. Yet, keeping all the beds open. Almost all the capacity scenarios are prepared to handle 100% of the demand, even though they do not staff based on 100% capacity. With the capacity scenarios, a cost and waste reduction can be achieved. (iii) Hybrid approach: this approach presents more waste than the capacity approach, but the risk of having less nurses than needed is reduced. Its disadvantage is the need of past reliable data. In conclusion, we advise to consider the capacity approach and, from this, the 85% capacity scenario. We consider that further research using simulation is recommended in order to simulate a more complex environment an evaluate the performance of the scenario. In case of willing to implement the new proposed methodology, we recommend to make a real life test, in which during a month the amount of nurses working is reduced. It is important to measure the motivation of the nurses before and after the test using questionnaires, as well as to measure the test performance. This will result in valuable information in decision making regarding the change of methodology. Even though the analysis is done based on the old ICD, taking into account the aforementioned assumption, it is possible to change the current staffi�ng methodology in the new ICD to improve the allocation of resources.
Item Type:Essay
Medisch Spectrum Twente, Esnchede, The Netherlands
Faculty:TNW: Science and Technology
Subject:01 general works
Programme:Health Sciences MSc (60022)
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