Smart Visit Prioritization and Rout Optimization for Efficient Home Palliative Care Services

Abstract - During a public health emergency, the demand for home health care rises significantly, with patients presenting a wide range of health conditions. To ensure that critical patients receive timely care, it is essential to essential to prioritize scheduling while adapting to unexpected urgent cases throughout the day. This article introduces a reorganization framework designed to dynamically adjust daily schedules for home health care providers. The goal is to maximize the total priority of visited patients while minimizing both overtime for healthcare providers and total travel time. To achieve this, I develop a Deep learning Machine Learning model that reevaluates the schedule at predetermined intervals. This ensures that urgent patients are accommodated on the same day whenever possible, while less critical visits are may be rescheduled to prevent excessive overtime.