AI-Supervised Home Palliative Care: A Comfort-First and Cost-Effective Alternative to Hospital-Based End-of-Life Care

This blog is based on my personal experience caring for my palliative mother, who at that time was expected to live less than two months. I am not a doctor. It reflects on how end-of-life care often brings unnecessary discomfort to patients, even when death is near. Palliative care should focus on comfort, dignity, and relief from symptoms—not on prolonging life through medical intervention.

Yet, hospital routines designed for safety can easily go too far. Nurses and doctors are required to follow strict protocols that call for frequent vital checks, blood tests, and continuous monitoring. Much of this comes from fear of legal responsibility rather than medical need. As a result, even patients in their final hours are often subjected to repeated procedures that offer no benefit but cause distress.

Many remain connected to machines until their last moments. Families watch their loved ones in pain, realizing that such interventions contradict the essence of palliative care. The system needs a clear shift—from monitoring by default to comfort by default.

AI could help make this shift possible. By using non-invasive data, it can alert clinicians when real attention is needed, reducing unnecessary tests and visits. When comfort-first principles are built into both policy and technology, patients can die peacefully while clinicians remain protected from liability.

In Korea, the national health insurance system makes most medical care affordable, but palliative services remain underdeveloped. Hospital fees are low, yet hidden costs—such as caregiver wages, room charges, and transportation—often exceed them many times over. Large hospitals frequently refuse to admit palliative patients, leaving families to choose between costly long waits or low-quality nursing hospitals.

A better answer lies in home-based, AI-assisted palliative care. Simple monitoring devices can balance usefulness with comfort—using wearables to measure heart rate and oxygen saturation, and bioimpedance or electrical impedance tomography (EIT) to observe fluid balance and muscle loss. Daily measurements, even if imperfect, reveal meaningful trends like dehydration or worsening edema. What matters is not precision, but the trajectory—small day-to-day changes that mirror the body’s final decline.

A low-cost handheld ultrasound device—about the size of a smartphone—can also be used at home. For patients near death, most necessary diagnostic tests have already been completed, so ultrasound should serve as a simple, inexpensive, and non-invasive supplementary tool rather than one requiring high precision.

An AI platform could gather all these data streams, summarize daily trends, and predict decline. Doctors would receive concise updates and visit only when truly needed, minimizing disruption. Even low-quality imaging could detect major problems like ascites or pleural effusion, helping guide simple relief procedures and providing peace of mind to families.

At first, this model should focus on patients expected to live less than a year and no longer receiving curative treatment. With explicit family consent, AI monitoring would be used only to support comfort, not prolong suffering. A shift toward home-based, AI-supported palliative care could restore the true spirit of medicine—helping people spend their final days in peace, comfort, and dignity. 

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