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Vol. 5 No. 1 (2024)
2024 Edition

Articles

Optimization of Nursing Staff Allocation in Elderly Care Institutions: A Time Series Data Analysis Approach

Published 2024-09-19

  • Daobo Ma
    • ,
  • Zhipeng Ling

Daobo Ma
Business Administration, California Institute of Advanced Management, CA, USA

Zhipeng Ling
Computer Science, University of Sydney, Sydney, Australia

Abstract

This study presents a new way to improve the distribution of nursing staff in nursing homes by analyzing research data. The general framework is designed to solve the complex problems of health management, including real-time data processing and updating the resource allocation process. The planning system uses real-time analysis techniques and machine learning algorithms to predict staffing requirements and optimize resource allocation. Experimental validation conducted across three large-scale elderly care institutions over 24 months demonstrates significant improvements in resource utilization efficiency. The framework achieves a 27.3% increase in staff allocation optimization and a 23.5% reduction in scheduling conflicts compared to traditional methods. The implementation incorporates multi-dimensional performance metrics, considering both quantitative operational efficiency and qualitative care quality indicators. Real-world case studies validate the system's effectiveness across diverse operational scenarios, with the model maintaining 94.3% accuracy in resource allocation predictions. The framework's adaptability allows for better responsiveness to operational changes while maintaining performance standards. This research contributes to the advancement of health management by developing new methods for data-driven staff allocation, ultimately improving performance good work, and good care in nursing homes.

Keywords

: Healthcare Resource Optimization, Time Series Analysis, Staff Allocation, Machine Learning

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