Index Evaluation of Different Hospital Management Modes Based on Deep Learning Model

Area of Science:

• Health Informatics
• Artificial Intelligence in Healthcare
• Hospital Management Systems

Background:

• Effective public hospital management is crucial for healthcare delivery.
• Traditional management systems often lack the efficiency and predictive capabilities needed for modern healthcare challenges.
• Optimizing resource allocation, particularly bed utilization, is a key area for improvement.

Purpose of the Study:

• To design and evaluate a novel hospital management index system using a deep learning model.
• To analyze the application and effectiveness of a reverse broadcast neural network in hospital public management.
• To identify key performance indicators for improving hospital operational efficiency.

Main Methods:

• Development of a hospital management index system incorporating a deep learning model.
• Application and performance analysis of a reverse broadcast neural network.
• Comparative analysis against classical algorithms for accuracy and delay.
• Weight analysis of management indices, focusing on bed utilization rates.

Main Results:

• The proposed deep learning model demonstrated higher accuracy and shorter delay than classical algorithms.
• The rational utilization rate of beds was identified as the most significant index in tertiary and secondary public hospitals.
• Model training showed high consistency between actual and predicted output values, with minimal residual error.

Conclusions:

• The deep learning-based hospital management index system effectively enhances public hospital management efficiency.
• The reverse broadcast neural network model shows significant promise for real-time performance analysis and prediction in healthcare settings.
• Prioritizing rational bed utilization is essential for optimizing the performance of public hospitals.