Client and Challenge

A regional hospital group in Bavaria faced a critical shortage of nursing staff (Pflegekräftemangel). Standard shift planning didn't account for the "acuity" of patients—meaning one day a ward might have 20 stable patients, and the next, 10 highly intensive ones. This unpredictability led to frequent staff burnout, high sick-leave rates, and expensive reliance on external temp agencies.

Solution

We implemented a Predictive Workload Integration module. Instead of just counting patient heads, the AI analyzes real-time clinical data (vital signs, medication frequency, and mobility scores) from the Electronic Health Record (EHR) to predict the actual nursing hours required for the next 24–48 hours. The system then suggests dynamic shift adjustments, ensuring staff are moved to the wards where the physical and mental load is highest.

Outcomes

• 18 % reduction in staff sick-leave days within the first year.

• €450k saved annually by reducing the need for expensive external "Leiharbeitskräfte" (temporary agency staff).

• Significantly higher staff satisfaction scores due to "fairer" and more predictable workloads.