Real-Time IoMT-driven Optimisation for Large-Scale Home Health Care Planning

The number of home caretakers is rising rapidly due to an increasing number of elderly people, recent pandemics, and the advancement of home health care facilities. Wearable medical devices and the Internet of Medical Things (IoMT) help health care managers monitor patients in real-time and provide remote medical care. This reduces home visits and helps Home Health Care (HHC) companies plan their resources. The paper addresses the HHC planning problem of allocating the optimal number of experts to patients while minimising the delay in visiting the patient, matching medical expertise with patient needs, and identifying the patient’s visit sequence. To tackle this, a new mixed-integer mathematical problem is proposed to reduce the total visit time for patients. This paper makes three key contributions towards tackling this plan, including (i) providing a formal definition of the problem and putting it in context with related work, (ii) proposing multiple problem instances varying in complexity, and (iii) an initial analysis of several heuristics and an exact solver (CPLEX) on these problem instances. The results indicated that the application of computational intelligence combined with IoMT can reduce patient visitation time significantly in a daily plan and therefore lead to 3.7 percent
improved care for HHC patients.