Coordination of Intraoperative Neurophysiologic Monitoring Technologist and Surgery Schedules

Area of Science:

• Healthcare Operations Research
• Medical Technology Management
• Surgical Workflow Optimization

Background:

• Surgical scheduling, particularly for specialized resources like Intraoperative Neurophysiologic Monitoring (IONM), faces significant coordination challenges.
• Inefficient resource allocation leads to increased costs, limited patient access, and compromised surgical quality.

Purpose of the Study:

• To develop and evaluate optimization-based algorithms for scheduling IONM surgical cases and technologists.
• To assess the impact of these algorithms on technologist staffing needs and resource utilization.

Main Methods:

• An iterative optimization model was developed in Excel, determining optimal surgery and technologist start times.
• The model's effectiveness was compared against current practices and alternative optimization scenarios using 10 days of operational data.

Main Results:

• The proposed model (scenario 4) reduced technologist overtime by 74% and under-utilization by 86%, decreasing staffing needs by 10%.
• Even partial optimization (scenarios 2 and 3) yielded substantial reductions in overtime and under-utilization.

Conclusions:

• Optimization-based approaches effectively improve IONM resource utilization and coordination.
• These methods enhance the efficiency of highly specialized resources within healthcare delivery systems.