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Decision Support Framework for Home Health Caregiver Allocation Using Optimally Tuned Spectral Clustering and Genetic Algorithm

Seyed Mohammad Ebrahim Sharifnia, Faezeh Bagheri, Rupy Sawhney, John E. Kobza, Enrique Macias De Anda, Mostafa Hajiaghaei-Keshteli, Michael Mirrielees

TL;DR

This paper tackles efficient caregiver allocation in Home Health Care under the constraint of flexible visit sequences. It introduces a decision support framework that combines optimally tuned spectral clustering with a genetic algorithm to cluster patients and guide allocations while prioritizing continuity of care and reduced travel. The approach demonstrates up to a 42% reduction in average travel mileage in a Tennessee case study and includes a caregiver-supply analysis to inform recruitment decisions. Practically, the framework provides a scalable, data-driven tool for HHAs to enhance scheduling efficiency, workload balance, and patient satisfaction, with potential applicability to other service sectors facing similar mobility and flexibility challenges.

Abstract

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.

Decision Support Framework for Home Health Caregiver Allocation Using Optimally Tuned Spectral Clustering and Genetic Algorithm

TL;DR

This paper tackles efficient caregiver allocation in Home Health Care under the constraint of flexible visit sequences. It introduces a decision support framework that combines optimally tuned spectral clustering with a genetic algorithm to cluster patients and guide allocations while prioritizing continuity of care and reduced travel. The approach demonstrates up to a 42% reduction in average travel mileage in a Tennessee case study and includes a caregiver-supply analysis to inform recruitment decisions. Practically, the framework provides a scalable, data-driven tool for HHAs to enhance scheduling efficiency, workload balance, and patient satisfaction, with potential applicability to other service sectors facing similar mobility and flexibility challenges.

Abstract

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.
Paper Structure (18 sections, 17 equations, 21 figures, 7 tables)

This paper contains 18 sections, 17 equations, 21 figures, 7 tables.

Table of Contents

  1. Introduction
  2. Background
  3. Decision support framework in HHCRSP
  4. Dynamics of HHCRSP
  5. Clustering approach for HHCRSP
  6. Problem statement
  7. HHC provider's concerns
  8. Problem formulation
  9. Refined allocation approach
  10. Clustering method
  11. Genetic optimization of clustering
  12. Decision support framework
  13. Computational Analysis
  14. Data pre-processing
  15. Case study results
  16. ...and 3 more sections

Figures (21)

  • Figure 1: The influential factors in increasing job satisfaction and the quality of care in HHC navaie2004increasing.
  • Figure 2: Change in demand for “Registered Nurses”, Pre and Post COVID-19.
  • Figure 3: A. Percentage of travel type count in a year, B. Percentage of travel type mileage in a year.
  • Figure 4: Current caregivers’ assignment vs. desired one.
  • Figure 5: Clustering of RN data using HDBSCAN
  • ...and 16 more figures