We developed a new forecasting engine to predict the hourly bed census and the daily arrivals (emergency, planned, yet to be planned and transfers) for each department for the next two weeks.
Having good forecasts is the corner stone of good capacity management. You can try our forecaster the first three months for free. Have a look at Forecasting Engine for more information.
We are very proud that we have helped more than 80 hospitals in 7 countries with our corona tool. The tool was made to provide a hospital more insights in the necessary level of bed capacity they would need when reaching the peak of the first wave of the covid crisis.
We are proud of the fact that we have been able to train many capacity advisor, managers, and even medical doctors into the field of capacity management and business analytics. Do you also want to get acquainted with the power of Business Analytics, or do you want to enrich your knowledge? Then take a look at our training offer. This training is given in Dutch.
De docent weet moeilijke dingen toch op een duidelijke en eenvoudige manier over te brengen.
We are developing small applications that can be used for specific purposes. Our first so-called use case is the Contact Center Staffing Calculator. A caller who calls to a contact center of, for example, an insurance company wants to speak to an employee of that company as quickly as possible. A hospital also wants to be easily accessible by Phone. If patients should wait too long, they may go to a different hospital. Determining how many ‘agents’ are necessary requires complicated calculations. Our calculator makes it easy for the user to calculate the right number of employees on the basis of a dataset. More information can be found in this dutch flyer. Simbox – use case – call center
The Erlang B formula is often used to calculate the blocking probability based on the average arrival rate, average service length and the number of servers. In hospitals it is important that patients are given the care that they need. This requires decision-making on the number of beds to use on each ward. Fully occupied wards mean that arriving patients cannot stay on their intended ward (they are blocked) and have to stay elsewhere with possibly less specific care. It is therefore that the Erlang B formula is often applied to dimension wards. Based on the average arrivals of new patients per day, the average length of stay and the number of beds, the Erlang B formula provides the chance that all beds are occupied. Hospitals should decide on the acceptable ‘blocking’ probability, but the occupancy level of the wards are also important. More beds result in lower blocking probabilities and lower occupancy levels at the same time. Both are important indicators for ward dimensioning.
However, the blocking probability as calculated by the Erlang B formula holds only for the situation in which patients arrive according to a Poisson process, meaning that the interarrival times should be exponentially distributed. If that is not the case, the use of the Erlang B formula to determine the number of necessary beds is discouraged. As an alternative, simulation offers the possibility to calculate the blocking probability for other type of arrival processes. The Simbox provides the most common distributions that can be used for modeling the arrival process. For arrival processes other than a Poisson process, simulation is really necessary in order to dimension wards in the right way.
For the Erasmus Medical Center in the Netherlands we provided an optimal blueprint for scheduling daycare patients using the Simbox application. To use the daycare beds as efficiently as possible, we have to solve a kind of tetris-like puzzle. Only here, we have to deal with variations in the length of stay of patients. That makes it more difficult. By using techniques from probability theory and optimisation, we have managed to determine optimal slots for different types of shots.