Surplus value quantification of overdue medical devices based on Kohonen network algorithm

Area of Science:

• Medical Technology Assessment
• Artificial Intelligence in Healthcare
• Health Informatics

Background:

• Overdue medical devices pose challenges in management, leading to resource waste and security risks.
• Efficiently assessing the residual value of aging medical equipment is crucial for optimizing healthcare resource allocation.
• Current methods for evaluating medical device surplus value may lack accuracy and efficiency.

Purpose of the Study:

• To develop and validate a quantitative model for evaluating the surplus value of overdue medical devices.
• To leverage the Kohonen network algorithm for accurate prediction of medical device residual value.
• To enhance decision support for effective medical equipment lifecycle management.

Main Methods:

• Utilized the Kohonen network algorithm to build a quantitative model for predicting the surplus value of overdue medical devices.
• Employed self-organization and data-driven learning capabilities of the Kohonen network for enhanced prediction accuracy.
• Compared the Kohonen network algorithm's performance against Support Vector Machine, Random Forest, and Decision Tree algorithms.

Main Results:

• The Kohonen network algorithm demonstrated superior prediction performance compared to Random Forest, with a maximum deviation of only 1.
• Achieved a significantly lower average error rate of 20.57% for the Kohonen network algorithm, compared to 46.34% for Random Forest and 65.31% for Decision Trees.
• The model effectively evaluated the correlation between service life and maintenance costs for various overdue medical devices.

Conclusions:

• The Kohonen network algorithm provides an effective method for quantitatively evaluating and predicting the surplus value of overdue medical devices.
• Implementing this algorithm can lead to improved medical equipment management efficiency and cost reduction.
• Accurate surplus value prediction contributes to enhanced patient safety by ensuring optimal equipment utilization.