University of Twente Student Theses
Developing Hospital@homes services at Rijnstate
Soontiëns, E.J.P. (2022) Developing Hospital@homes services at Rijnstate.
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| Abstract: | Context Recently, Rijnstate started developing their ‘Virtueel Zorgcentrum’ (VZC). The VZC is a virtual care and monitoring centre used to monitor patients in their home situation (Rijnstate, 2020). It is not yet known how the hospital care at home can best be organised. A master thesis has been conducted by Stoker (2019), which laid the foundation for the development of the hospital care at home at Rijnstate. It remains unclear whether the care at home can best be provided by Rijnstate alone, or if external (home care) organizations should be involved to obtain the required capacity. Rijnstate would like to investigate how hospital care at home can be organized as effectively as possible in order to keep the care demand towards hospitals manageable, to promote the efficiency of care provision and to increase patient satisfaction. Goal To (1) perform a benchmark of hospital@home services and the link with hospital control centres in a number of large (STZ) hospitals, and to (2) develop a quantitative model that can prospectively assess and compare the previously defined service designs and can be used for the further development of the Rijnstate@home services in terms of capacity management. Approach The research consists of two parts, both contributing to the development of the model and Rijnstate’s @home services. First, a comparison of hospital@home services within the mProve network is made following the benchmarking method of Van Hoorn et al. (2006). This benchmark provides insight into the current developments of this new type of care provision in the Netherlands and creates a solid foundation for the second part. After performing a literature search, we find that simulation is the most appropriate modelling technique. In the second part of the research, the simulation model is created following the steps for a simulation study as described by Law (2014). Results The most important findings of the benchmark are: • No variety exists in the service design used by the benchmarking partners for the provision of virtual care, meaning that the way virtual care is delivered to the patient is organized in the same way. When a patient is monitored at home, he or she should visit the hospital when a complication occurs and is treated at a certain specialty. • The different characteristics of the patient groups and initiatives make it harder to compare services across hospitals. ii • Although the path of development of the provision of care at home is different in each hospital, the goal of each hospital is to centralize initiatives in the long term. • Combining different hospital@home services in an unexplored area in most hospitals. • The amount of available (quantitative) data is not very extensive. Measuring and showing results can be a great incentive for people to devote more time to the initiatives for providing hospital care at home. The most important findings of the conducted experiments are: • The decision on the number of nurses should be based on the utilization as well as the waiting time in queues prior to the involved server. The simulation model can assist the user in making this trade-off. • Nurses should have multiple tasks, since the utilization rate is very low when solely treating patients with complications. When a larger scale is reached, the issue of the low utilization rate will be no object. • Including only outpatient groups requires less time for patient admission, since less unique patients are admitted. The utilization of nurses is higher because the number of outpatients included at the same time is greater than when inpatients are included. • The performance of the system depends on the arrival rate and length of stay of the individual patient groups. This emphasizes the importance of the choice of input parameters. When the arrival rate or length of stay of one of the patient groups has been estimated incorrectly, this significantly changes the output. Conclusion The benchmark has been performed amongst the hospitals in the mProve network. The field truly is still in the development phase in most hospitals, resulting in a small amount of available data. No uniform pattern or definition of patient groups is found. This also results in a discrepancy in required resources. The large variety of goals and initiatives can also be used to our advantage. In this way, we can compare which type of care delivers the best quality of care, financial advantage, positive patient experience, and employee satisfaction, if more data becomes available in the future. The simulation model can be used for the intended purposes. The model is validated together with stakeholders and the experiments yielded the expected results. At this point the input data is not reliable enough to make detailed analyses. It is possible to compare scenarios and designs, but choices for parameters (such as the number of nurses) cannot be accepted directly. The experiments give insight into the impact of the number of patients and the patientmix, for example the difference between virtual care for inpatients and outpatients. Furthermore, they provide information about the considerations to make when choosing parameters and the influence of input data on the results. Discussion The two main issues experienced during this research were the lack of data and the discrepancy between different definitions. A large amount of the input data is based on expert opinions and data from other processes. When the input data is retrieved from the reality, this will increase the reliability of the model and enable the user to perform more detailed analysis. In particular the data related to the patient groups. It became clear from the sensitivity analysis that this input data directly affects the output of the model. The need for uniform definitions and parameters resulted from both the benchmark and the simulation iii model. Making a direct comparison between hospitals is hard, since the patientmix within the initiatives is different across hospitals. Finding rules of thumb, such as the number of patients per nurse, is not possible because of this. The impact of including different patient groups in the service is also visible in the experiment results. To increase the comparability of hospitals, the mProve network should aim to develop a universal definition of virtual care and parameters that can be measured across all its hospitals. |
| Item Type: | Essay (Master) |
| Faculty: | BMS: Behavioural, Management and Social Sciences |
| Programme: | Industrial Engineering and Management MSc (60029) |
| Link to this item: | https://purl.utwente.nl/essays/90670 |
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