Optimization of medical radiation technologist schedules using advanced analytical tools.

INTRODUCTION/BACKGROUND: Medical imaging departments are facing significant workforce challenges due to a shortage of medical radiation technologists (MRTs), leading to increased wait times and staff burnout. Traditional manual scheduling methods are time-consuming, prone to error, and contribute to staff dissatisfaction. To address these operational challenges and improve clinical workflow, a quality improvement initiative was undertaken to optimize MRT scheduling using advanced analytical tools.

METHODS

A cost-constrained optimization model was developed using Microsoft Excel's Solver tool. Staffing data from the University Health Network (UHN) medical imaging department served as the basis for model design. Key constraints included staff availability, fairness in shift assignments, overtime cost minimization, and maximum consecutive shifts. The model incorporated full-time, casual, and agency staff, with an emphasis on equitable work distribution and cost control.

RESULTS

The optimized scheduling model successfully created a fair, fully staffed 4-week schedule while minimizing costs. Full-time MRTs were assigned 40-hour work weeks without exceeding contractual limits, and agency and casual staff were effectively integrated to prevent overtime. The model reduced the time required to generate schedules and minimized common errors such as double-booking and uneven shift distribution.

DISCUSSION

The use of an advanced analytical approach for MRT scheduling demonstrates a practical, scalable solution for healthcare organizations. By aligning shift assignments with operational demands and human resource principles, the initiative supports staff well-being, promotes workplace fairness, and contributes to improved patient care delivery. Importantly, this method is cost-effective and can be adapted to other clinical departments facing similar staffing and scheduling challenges.

CONCLUSION

This quality improvement initiative highlights the potential for healthcare departments to leverage simple yet powerful optimization tools to enhance clinical operations. The successful implementation of an analytical scheduling model in a high-volume medical imaging department underscores the value of evidence-informed process improvements at the frontline of clinical practice.