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• W2623793314 abstract "In the framework of completing the master thesis of Health Sciences, I performed research atthe Intensive Care Department (ICD) of the Medisch Spectrum Twente (MST) into studyinghow to improve the sta�ffing of nurses.In January 2016 the MST moved into a new building. With this change, the ICD was expandedand the capacity increased from 28 beds -in the past situation- to a maximum capacity of 42beds. The units also changed. In the past situation there was one Thorax and one Generalunit, whereas in the new situation there is one Thorax unit, two General intensive care unitsand one Medium care unit.With the change of situation, the medical manager from the ICD thought about the possibilityof adjusting staffi�ng to the activity, with the purpose of increasing the e�fficiency, because hehad the impression that there were more nurses than needed. He also had the feeling thatwhen there are more nurses than beds to cover the motivation of the nurses decreases. Untilnow, the methodology used for sta�ffing is based on the bed capacity. Thus, he wanted tostudy 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 nursestaffi�ng purposes can be improved. Not only do we consider staffi�ng based on demand, butwe 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 CareDepartment of the MST?In order to find an appropriate approach for answering the research question, we have to takeinto account that the ICD underwent an important expansion. There was not enough datafrom the new situation to extract conclusions about possible changes. Moreover, the datawas not reliable. Together with the manager, the following assumption was agreed upon: ifan approach would have been effective in the old ICD, it will be effective in the new ICD aswell. 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 datafrom 2012 to 2015. The most relevant information found was that the most common bedoccupancy rates were between 71% and 90%.In this project we consider three staffi�ng approaches: sta�ffing based on demand, based oncapacity and based on a hybrid approach. Within each approach, we consider differentscenarios. These approaches and scenarios are: (i) Demand approach: the demand approach consists in predicting the daily average numberof beds occupied per week in 2015. The scenarios considered are: perfect prediction,naive forecast, moving averages and seasonal indexes. We also forecasted the demandusing ARIMA models. Even though we found mathematically correct models, theprediction was close to the mean of the data and did not show variations. Since we werenot satisfied with the results, and the application of ARIMA models is time-demandingand requires forecasting skills, we decided to develop heuristics for demand prediction.The heuristics developed are the aforementioned demand scenarios (except for the perfectprediction).(ii) Capacity approach: the capacity scenarios calculate the number of beds occupied basedon a percentage of the maximum available capacity. The different scenarios consideredare: 100%, 85%, 80%, 75% and 70% of capacity. For example, 85% of capacity meansthat we assume that 85% of the beds are occupied, even though 100% of the bed capacityis available.(iii) Hybrid approach: it is a combination of capacity and demand staffi�ng. First, staffi�ng isdone based on a percentage of the maximum available capacity. Then, a reinforcementof this staffi�ng is done based on demand forecasting (using seasonal indexes). Thismeans that the hybrid approach takes the maximum value between the capacity and thedemand approach. The scenarios are: 85%, 80%, 75% and 70% hybrid.The presented scenarios were discussed from three perspectives: waste, financial and practicalpoint 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 the75% capacity scenario, with a pool of nurses of 61.68 FTE. This implies 20.6 FTE less thanusing the 100% capacity scenario.From a financial point of view, the 70% capacity scenario is the best one due to the reducedcosts. Taking into account the pool of nurses and extra hours, the 70% capacity scenario addsup to a total amount of 1,844,296 e, whereas sta�ffing based on 100% capacity costs 2,617,600e. This supposes a cost reduction of 29.5%. Nevertheless, due to the lack of nurses thatthis scenario implies, and that the resulting nurse-patient ratios differ significantly from theoptimal-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 beensimplified, thus, the scenarios mentioned so far might be too adjusted and not appropriate tobe implemented. Therefore, taking into consideration the fact that changes are diffi�cult andthat reality has been simplified, we consider that a scenario closer to 100% capacity might bea 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 unnecessaryhours of work that it has. Moreover, a considerable amount of reliable past data isneeded, which is often diffi�cult to obtain. (ii) Capacity approach: it is possible to improve the current staffi�ng methodology using thisapproach, 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 handle100% of the demand, even though they do not staff based on 100% capacity. With thecapacity scenarios, a cost and waste reduction can be achieved.(iii) Hybrid approach: this approach presents more waste than the capacity approach, butthe risk of having less nurses than needed is reduced. Its disadvantage is the need ofpast reliable data.In conclusion, we advise to consider the capacity approach and, from this, the 85% capacityscenario. We consider that further research using simulation is recommended in order tosimulate a more complex environment an evaluate the performance of the scenario. In caseof willing to implement the new proposed methodology, we recommend to make a real lifetest, in which during a month the amount of nurses working is reduced. It is important tomeasure the motivation of the nurses before and after the test using questionnaires, as well asto measure the test performance. This will result in valuable information in decision makingregarding the change of methodology.Even though the analysis is done based on the old ICD, taking into account the aforementionedassumption, it is possible to change the current staffi�ng methodology in the new ICD toimprove the allocation of resources." @default.
• W2623793314 created "2017-06-15" @default.
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• W2623793314 date "2017-01-01" @default.
• W2623793314 modified "2023-09-26" @default.
• W2623793314 title "Staffing won't be worse with one fewer nurse: Improving the staffing of nurses in the Intensive Care Department of the Medisch Spectrum Twente" @default.
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