Optimization of medical radiation technologist schedules using advanced analytical tools

Area of Science:

• Healthcare Operations Management
• Health Informatics
• Operations Research in Healthcare

Background:

• Medical imaging departments face critical workforce shortages of medical radiation technologists (MRTs).
• Manual scheduling methods are inefficient, error-prone, and contribute to staff burnout and dissatisfaction.
• These challenges negatively impact patient wait times and overall clinical workflow.

Purpose of the Study:

• To optimize medical radiation technologist (MRT) scheduling in a medical imaging department.
• To address operational challenges and improve clinical workflow through advanced analytical tools.
• To develop a scalable and cost-effective scheduling solution.

Main Methods:

• A cost-constrained optimization model was developed using Microsoft Excel's Solver tool.
• The model utilized staffing data from a university health network's medical imaging department.
• Key constraints included staff availability, shift fairness, overtime cost minimization, and maximum consecutive shifts, incorporating full-time, casual, and agency staff.

Main Results:

• The optimized model generated a fair, fully staffed 4-week schedule while minimizing costs.
• Full-time MRTs received 40-hour work weeks within contractual limits, and agency/casual staff prevented overtime.
• Schedule generation time was reduced, and common errors like double-booking were minimized.

Conclusions:

• Advanced analytical scheduling offers a practical, scalable solution for healthcare organizations facing staffing challenges.
• This approach enhances staff well-being, promotes fairness, and improves patient care delivery.
• The cost-effective method is adaptable to other clinical departments with similar scheduling needs.