Automated and Optimized Neurosurgery Scheduling System Improves Resident Satisfaction.

Area of Science:

• Medical Education
• Neurosurgery Residency Training
• Healthcare Operations

Background:

• Neurosurgery residency scheduling is complex, often manual, and prone to bias and perceived unfairness.
• Challenges include managing multiple hospitals, services, and clinic days, impacting equitable call distribution.
• Existing systems struggle to accommodate resident requests and optimize call variation.

Purpose of the Study:

• To develop and evaluate an automated scheduling system (AONSS) for neurosurgery residency.
• To reduce scheduling biases and improve fairness and resident satisfaction.
• To optimize call variation and enhance the educational experience through diverse junior-senior-attending relationships.

Main Methods:

• Developed and implemented the Automated Optimization of Neurosurgery Scheduling System (AONSS) at the University of Florida.
• Used 2019-2021 academic years as a control group for comparison.
• Surveyed residents pre- and post-implementation to assess satisfaction, fairness, bias, call variation, pairing, and duty hours.

Main Results:

• Overall call variation reduced by 70% with AONSS implementation.
• Resident satisfaction increased from 21% to 90%; fairness perception rose from 43% to 95%.
• Perception of gender bias decreased from 29% to 0%; duty hours were reduced and less varied.

Conclusions:

• The AONSS effectively reduces call variation in neurosurgery residency.
• The system significantly increases resident satisfaction and perception of fairness.
• AONSS eliminated perceived gender bias and was associated with decreased duty hours.