The Emergency Room (ER) at Zuyderland Medical Center receives, examines and treats all patients with emergency conditions. It is the most active department within the hospital with a constant flow of incoming patients suffering from an acute illness or injury. The ER is typically staffed with nurses and physicians who treat a wide spectrum of medical conditions, including cardiac arrests, strokes, serious injuries, pregnancy complications, asthma and various other conditions.

To enhance its patient care, Zuyderland aimed to use AI and machine learning to anticipate patient admissions to the ER. The nature of this ward is often associated with a high-stress medical environment, where every second counts. By accurately predicting patient admissions, Zuyderland wants to allocate resources efficiently and ensure they have the right number of medical staff, beds, equipment and medications available to immediately address incoming cases. During this project, a Data Science expert from Mediaan Conclusion supported Zuyderland with:

As we took into account variables like weather conditions and holidays, we had to make short-term bi-weekly forecasts to ensure the accuracy of our predictions. To do this, we needed models that could quickly adapt to this type of data. Another challenge was that our historical data was limited, spanning only a few years. For instance, in a year with no snow, it was impossible to make reliable predictions since we had no relevant samples. This limitation naturally had an impact on our prediction results. Conversely, when we had complete data, our model was able to make accurate predictions. Another challenge was breaking down our forecasts by specialty within the hospital workforce to determine the manpower needed at specific times.