Decision Support Framework for Home Health Caregiver Allocation Using Optimally Tuned Spectral Clustering and Genetic Algorithm
This addresses the challenge of efficient caregiver coordination for home health agencies, which is crucial for budget optimization and quality care delivery, though it is incremental as it builds on existing methods with a focus on flexibility.
The study tackled the problem of optimizing caregiver allocation in home health care by introducing a decision support framework that allows flexible visiting sequences, resulting in up to a 42% reduction in average travel mileage without imposing restrictions on caregivers.
Population aging is a global challenge, leading to increased demand for health care and social services for the elderly. Home Health Care (HHC) is a vital solution to serve this segment of the population. Given the increasing demand for HHC, it is essential to coordinate and regulate caregiver allocation efficiently. This is crucial for both budget-optimized planning and ensuring the delivery of high-quality care. This research addresses a fundamental question in home health agencies (HHAs): "How can caregiver allocation be optimized, especially when caregivers prefer flexibility in their visit sequences?". While earlier studies proposed rigid visiting sequences, our study introduces a decision support framework that allocates caregivers through a hybrid method that considers the flexibility in visiting sequences and aims to reduce travel mileage, increase the number of visits per planning period, and maintain the continuity of care; a critical metric for patient satisfaction. Utilizing data from an HHA in Tennessee, United States, our approach led to an impressive reduction in average travel mileage (up to 42%, depending on discipline) without imposing restrictions on caregivers. Furthermore, the proposed framework is used for caregivers' supply analysis to provide valuable insights into caregiver resource management.