AZ Sint-Lucas is a major general hospital located in Ghent, Belgium, with over 700 beds, 2,000+ nursing staff, and a strong reputation for delivering acute, surgical, and chronic care to a diverse patient population. Since May 2024, the hospital has been using Team n Time to gain real-time, objective insights into total nursing workload and optimize staff deployment accordingly.
What began as a response to limited visibility has evolved into a powerful case of operational transformation, improving efficiency, flexibility, and staffing precision across the organization.
The challenge: no clear view of true workload
Before implementing Team n Time, staffing decisions at AZ Sint-Lucas were based on bed occupancy and subjective impressions of care demand. Requests for float pool support followed a “first come, first served” model, often leading to inefficient coverage and care mismatches.
“We lacked an objective way to understand where workload was truly high,” explains Gerta Haxhiaj, a member of the hospital’s care development team. The introduction of Team n Time in May 2024 marked a turning point, though the shift required trust, adaptation, and focused change management.
The solution: real-time insights and smarter allocation
Team n Time now provides AZ Sint-Lucas with continuous, shift-by-shift visibility into nursing workload across the hospital.
“Departments can submit support requests up to five days in advance,” says Nele de Smul, float pool manager. “And float shifts are now scheduled based on real workload instead of assumptions.”
Where staffing used to be reactive, the hospital now uses predictive, data-driven planning. One of the biggest benefits is hospital-wide transparency. “You might have a unit that looks full on paper,” Nele explains, “but if most patients are independent, we can reallocate staff where the workload is higher.”
This allows managers to respond flexibly to daily or weekly shifts in care needs. It also enables smoother collaboration and staff sharing between internal medicine, geriatrics, and surgical units, something that was much harder before.
Implementation: change management done right
The transition wasn’t without challenges. “It was really about building trust and helping teams get used to the system,” says Nele.
Unit leaders had to learn how to use the platform and ensure accurate, timely data entry. “It was a learning process,” adds Gerta. Emma Verheirstraeten, also on the care development team, highlights how the phased rollout, starting with a few core units and expanding gradually, helped manage change more effectively.
Tailored KPIs built confidence, and regular communication kept the tool top of mind. As usage grew, so did data quality. Even patient records are now being documented more thoroughly, leading to better workload assessments.
The impact: efficiency, flexibility, and confidence
Today, staffing decisions reflect actual patient care needs rather than just headcounts. “The trust that internal medicine and surgical departments now place in the workload data is a major step forward,” says Gerta.
Float pool scheduling is now more strategic, and unit managers are able to deploy staff where they are truly needed. “We’re operating with more precision and agility,” Nele adds. This approach supports more consistent care and smoother day-to-day operations.
Looking ahead: scaling the benefits
The improvements so far are just the beginning. As of May 2025, additional departments have joined the system, and the float pool is on track for full integration. Soon, staff will be able to view their real-time assignments directly in Team n Time, making deployment even more efficient.
More staff members will also gain access to the platform, which will help adoption grow across the hospital. Nele’s advice: “Look at the data. It speaks for itself. This is a powerful tool.”
Conclusion
Team n Time has helped AZ Sint-Lucas make a meaningful leap in workforce management. With real-time visibility into total workload and a data-driven approach to staffing, the hospital has gained efficiency, flexibility, and cross-team collaboration. The implementation required strong change leadership, but the results are clear: a smarter, more responsive model for hospital care, with even more potential ahead.