867. Integrating IoMT and Multi-Objective Optimisation for Efficient Expert Allocation in Home Health Care
Invited abstract in session TA-11: Home Health Care, stream OR in Healthcare (ORAHS).
Tuesday, 8:30-10:00Room: Clarendon SR 1.03
Authors (first author is the speaker)
| 1. | Seyedamirhossein Salehiamiri
|
| Alliance Manchester Business School, The University of Manchester | |
| 2. | Richard Allmendinger
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| Alliance Manchester Business School, The University of Manchester | |
| 3. | Arijit De
|
| Alliance Manchester Business School, University of Manchester |
Abstract
The increasing ageing population has demanded more investment in different healthcare sectors, especially the home healthcare area. However, more experts are leaving the industry since the current situation offers less flexibility for nurses, more pressurised roles, and riskier conditions for patients. Meanwhile, newly developed health care systems, such as the Internet of Medical Things and wearable devices, can help automate tasks such as recording and storing real-time patient data and performing necessary care where possible. This paper addresses the problem of allocating experts to patients considering time windows, the expert's skill level, and the service menu, considering multiple objectives. The primary objective is to minimise the total service menu, while the second objective maximises the fairness of the distribution of the most complex tasks; finally, the third objective minimises the total service time for the patients with the highest risk levels. The proposed approach employs two solution approaches, the epsilon-constraint and the multi-objective evolutionary algorithm based on decomposition. The results from different scenarios indicate the decrease in patients visit time and balance in the expert's workload across the home health care industry.
Keywords
- Health Care
- Combinatorial Optimization
Status: accepted
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