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. 

Example: Patient-Centered Bioimpedance Monitoring 

Bioimpedance monitoring offers a non-invasive, low-burden, and repeatable approach to physiological surveillance in elderly patients receiving palliative care. Its primary value lies not in diagnostic precision, but in supporting informed clinical decision-making aligned with comfort-focused care.

Rather than targeting absolute values, bioimpedance is used to track within-patient directional changes over time. When measurements are obtained under consistent conditions, repeated assessments can provide meaningful insights into fluid status, peripheral edema, markers associated with progressive muscle loss, and overall physiological decline—without reliance on invasive procedures such as blood sampling or imaging.

Bioimpedance data should be interpreted in conjunction with simple clinical context, including diuretic use, fluid intake, dyspnea, visible edema, and reduced mobility. This contextual integration enhances interpretability while minimizing patient burden. In this setting, AI-assisted trend summarization enables clinicians to rapidly review longitudinal changes and make timely, comfort-oriented adjustments, thereby reducing unnecessary interventions.

When developing biosignal monitoring systems for palliative care, a fundamental question must always be asked: “Will this test result actually make the patient more comfortable?” Palliative care does not mean abandoning treatment. Rather, it represents a care strategy that deliberately removes interventions that do not meaningfully benefit the patient. When test results do not alter clinical decision-making and repeatedly lead to the conclusion of “no change in management,” routine laboratory investigations that merely accumulate numerical data should be discontinued.

In this context, judiciously applied biosignal monitoring can play a meaningful role in home-based palliative care, where hospitalization often leads to physical deconditioning, cognitive disturbance, and accelerated muscle loss. When used appropriately, such monitoring supports continuity of care outside the hospital and contributes to maintaining patient dignity and an element of elegance in care delivery. Importantly, the goal is not the optimization of numerical targets, but the preservation of comfort, stability, and reassurance.

Hospitalization itself represents a significant physiological and cognitive stressor for elderly patients. Among its well-recognized consequences is delirium, which occurs frequently in older adults and is often driven by environmental disruption, sleep deprivation, medication effects, and reduced mobility rather than by direct progression of the underlying disease. This vulnerability reflects the limited physiological reserve of the aging brain and helps explain why hospitalization can worsen patient experience without proportionate clinical benefit. From a palliative care perspective, these considerations reinforce the importance of minimizing unnecessary hospitalization and favoring care delivered in familiar, home-based environments whenever feasible. By reducing avoidable medical stressors while maintaining symptom-oriented surveillance through appropriate biosignal monitoring, home-based palliative care better aligns with the fundamental aims of palliative medicine: comfort, dignity, and psychological stability.

To make bioimpedance monitoring feasible in elderly palliative care, the key bottleneck is not the electronics but the electrodes. Developing wearable electrodes that can be applied in seconds, are skin-safe for fragile patients, and provide reproducible contact quality is essential for low-burden, trend-based monitoring.  Using multi-frequency bioimpedance (three or more frequencies) improves measurement consistency compared to single-frequency approaches by reducing sensitivity to contact-related variability and allowing more robust interpretation of longitudinal trends, particularly in settings with fluid shifts.