Effective Care Planning with Health Intelligence

Prinses Máxima Centre

This paediatric oncology center’s mission is to cure every child from cancer while maintaining optimal quality of life. The Princess Máxima Center aims to maximize care and attention for young patients and their families by scheduling its professionals and allocating its resources as efficiently as possible. Turner helped to further this aim, using data to get a handle on staffing levels and utilization, for example matching supply and demand for radiology services, treatment rooms and consultations. We also initiated active management of peak and valleys in patient volume. This gave the center greater control of planning and workload for its staff.

Peter de Bruin Square

“We’re proud of the positive results we achieved with Health Intelligence”

– Peter de Bruin, Turner

Towards Health Intelligence

More effective planning was achieved by a series of steps we helped the primary managers to take:

1. From Data to Information

We began by giving health care workers and planners more insight, using dashboards that showed production levels and utilization of capacity. The visualized data included bed occupancy in a specific department, the number of scheduled outpatient appointments, and the overcrowding at the radiology department, for instance.

2. From Information to Management

The next step was to analyze and solve the bottlenecks in staffing and patient planning. In terms of patient volume, for instance, we analyzed what percentage of patients who presented for consultation would continue on for treatment. This information enables redistribution of the flow to avoid peak times and minimize waiting time. These and other insights were discussed with the clinical teams.

3. From Management to Control

The last step of Health Intelligence was to learn how to predict bottlenecks. This clearly helps health care workers to plan care more effectively, and take into account predictable variations in patient volume due to holidays or school vacations, for example, or factoring in fluctuating requests for support depending on a specialist’s presence of absence. Predictable patterns like these enable the center to schedule their staffing more effectively and spread hospitalizations to optimize care for individual patients. This makes it easier to control waiting lists and staffing levels.

labcoats in lab

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