Blockchain-IoT-Driven Nursing Workforce Planning for Effective Long-Term Care Management in Nursing Homes

Area of Science:

• Gerontology and Health Services Research
• Information Technology in Healthcare
• Operations Research

Background:

• Global population aging increases demand for elderly long-term care, straining nursing homes.
• Professional shortages in nursing homes create significant pressure on elderly care services.
• Current systems lack integrated solutions for monitoring care and optimizing nursing staffing.

Purpose of the Study:

• To develop an integrated solution for monitoring elderly care services and optimizing nursing staffing in nursing homes.
• To formulate a nursing staffing optimization model within a blockchain-internet of things (BIoT) environment.
• To address the challenges of professional shortages and workload balance in long-term care.

Main Methods:

• Utilized a blockchain-internet of things (BIoT) environment to create a decentralized long-term care platform.
• Employed a genetic algorithm for nursing staffing optimization based on monitored care data.
• Integrated the concept of skill substitution into the staffing optimization process.

Main Results:

• A case study in a Hong Kong nursing home demonstrated the system's effectiveness.
• The proposed BIoT model resulted in a 13.48% reduction in total monthly staffing costs.
• Enhanced care monitoring, staffing flexibility, and workforce heterogeneity were achieved.

Conclusions:

• The BIoT-based nursing staffing optimization model offers a viable solution for long-term care challenges.
• The system effectively reduces costs, improves monitoring, and increases staffing flexibility.
• This approach supports sustainable and efficient elderly care services amidst workforce pressures.