At the moment, it is mainly waves of illness that are ensuring that laboriously devised shift plans repeatedly have large gaps. This problem is exacerbated by the difficulty of filling all positions, even in service areas. In future, it is likely that available resources will not match the actual order volume and will therefore become the norm. Our case study on discharge cleaning shows how great the potential for optimization is, even if no more resources are available: Process data can be used to optimize shift schedules in order to significantly reduce the proportion of open orders. This provides noticeable relief on the ward during discharge and new occupancy. Without any increase in personnel.
Note: The analysis can be applied to all simplinic process solutions.
As in our case study of decentralized discharge cleaning at a hospital with over 600 beds: The shift schedule gives a potential of 440 bed reprocessings per week, but the average order volume of the last few weeks was 470.
The result can be clearly visualized: Over the course of the day, a high number of "open" orders is the norm with the current shift plan. In practice, this means that a smooth flow of patients is almost impossible without the support of the nursing staff to prepare the beds and spaces themselves. The process therefore causes complaints.
An increase in cleaning resources was not possible in the short term and a reduction in occupancy pressure was not to be expected. As our cleaning control software provides exact data on requirements and processing, the data basis for the order volume is available exactly for each hourly slice. This is a crucial basis for process optimization, as discharge patterns on the ward follow certain patterns, but are by no means distributed exactly evenly throughout the day and are never complete by 12:00 noon: there are inevitably peak and off-peak times, and staff can only be flexibly allocated within narrow limits.
We therefore use mathematical methods to optimize this distribution problem in order to model the optimum actual demand and resource allocation. The model is able to calculate the exact resource requirements for a desired service level. In this case study, however, we are only concerned with optimizing the existing team.
In addition to the actual orders per hour, we use the so-called "handling time", i.e. the realistic processing time for bed preparation. Here, it is essential to take into account the special features of the individual hospital process, such as whether cleaning staff always work in teams of two, whether prepared bed packages can be stored on the ward, etc.
The model result for the 600-bed hospital was the listed shift plan with exactly the same staff deployment. The availability of staff was agreed with the customer and "limit values" for a maximum team utilization of 80% were taken into account. It is often supposedly "soft" factors that determine whether a model passes the practical test: many employees can only work in the mornings because children have to be looked after in the afternoons, and public transport timetables in rural areas sometimes limit the flexibility of work deployment.
The effect of shift planning can be checked immediately in the model: The change in operating times has a significant impact on the distribution of open orders despite the same personnel resources. On average, the service level improves by 20-25%.
Even with scarce resources in the service areas, the service level can be significantly optimized with the right tools.
The Berlin Chamber of Industry and Commerce's Skilled Labor Monitor predicts that supply on the labor market will no longer meet demand in the coming years. There are limited opposing trends, such as immigration or consolidation of demand in times of low growth. However, the demographic trend is irreversible and will require all employers to ensure overall productivity in hospitals with the available workforce.
The case study shows the leverage that digitalization has in the service areas of the hospital: even with scarce resources, problems such as a bow wave of unfinished cleaning orders can be reduced. Without digitalization of this process, the data for an optimization analysis would not be available.
Our analyses are available at any time, andwe can quickly identify deficits and suggest solutions in a one-hour meeting with facility managers or specialist managers. In our view, good data and trusting collaboration in different roles are important prerequisites for ensuring that hospital operations run reliably overall in the face of increasing staff shortages.
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