A country’s healthcare system is a critical infrastructure that requires long-term capacity planning. To plan optimally, avoid logistical problems and take more effective preventive measures, having insight into the number of available hospital beds and patient movements is of the utmost importance.

Moving patients from one ward to another involves more than it seems. Departments often place patients temporarily in free beds from sister wards until their own beds become available. This requires timely coordination, so an early request would reduce logistical problems. With high patient inflows, the hospital must have enough staff to ensure that patients can be properly accommodated and receive clinical care. Last but not least, by gaining insight into the right information, hospital staff could take more efficient preventive action, for instance by carrying out more discharge rounds when there is a peak of patients coming in.

During this project, our Data Science experts supported our client, a hospital located in the south of the Netherlands, with:

To overcome all the challenges and provide our client with the best possible solution, the team created a hybrid model. We had to ensure that the model could not only predict the number of patients admitted to the hospital, but also indicate upper and lower thresholds with a degree of confidence. This would allow you to strategically plan for worse-case scenarios, so that even if you end up admitting more patients than statistically expected, you still have enough capacity. The information obtained from this prediction would then be used as input to simulate the movement of patients from one ward to another. We then developed a user-friendly dashboard that provides a clear and better overview.