Cardiac surgery productivity and throughput improvements.

Area of Science:

• Cardiovascular Surgery
• Health Systems Management
• Operations Research

Background:

• Variability in open-heart surgery duration poses challenges for managing operating theatre productivity.
• Accurate prediction of surgery length is crucial for efficient resource allocation and staff management.

Purpose of the Study:

• To evaluate the impact of six interventions on open-heart surgery operating theatre productivity.
• To assess the effectiveness of a linear regression model for operation time forecasting and identifying key influencing factors.

Main Methods:

• A discrete event simulation model using empirical data from 2603 patients.
• Linear regression analysis to forecast operation times and explain time variance.

Main Results:

• A linear regression model explained 46% of operating time variance, with operation type, repeat surgeries, and head surgeon as key factors.
• Reducing changeover times (e.g., anesthesia outside OR) and end-of-day slack time showed the strongest positive impact on productivity.
• Improved forecasting decreased overtime but did not increase surgery output.

Conclusions:

• The developed forecasting model can identify significant factors influencing open-heart surgery length.
• Strategies like optimizing scheduling for shorter surgeries and reducing inter-surgery time can improve the feasibility of performing two surgeries daily.
• Process interventions, particularly those reducing changeover times, are more effective for enhancing productivity than improved time forecasting alone.