AI in Long-Term Care

Long-term care facilities manage extensive resident documentation that must stay current, e.g., ADL assistance levels. AI-supported solutions can save hours of staff effort each day. GenAI agents and multi-agent systems in an LTC EHR can turn dictated notes into structured entries, generate summaries for care handoffs, and flag missing documentation. They can also propose personalized care plans based on the resident record and facility templates and submit them for staff review. Such drafts can then automatically feed daily task lists for the staff that reflect the residents’ current status, required assistance, and cognitive training goals.

You can find a sample Azure-based architecture for the EHR orchestration layer that supports GenAI agent workflows on the AI-native EHR page.

GenAI-powered virtual assistants help people receiving long-term care handle daily tasks through natural voice or chat conversations. They can set medication reminders based on schedules entered by a care recipient, a family caregiver, or care staff, schedule home visits or appointments, and send routine updates to designated family contacts and the care team. For education, assistants can answer questions using approved facility materials and care protocols, so guidance stays consistent for chronic conditions such as diabetes or Alzheimer’s disease.

Voice and chat conversations can provide companionship, prompt cognitive activities, and suggest music, videos, and audiobooks that match preferences and cognitive ability. With GenAI-guided check-ins, residents can answer prompts about mood and symptoms, and the assistant summarizes what changed since the last check and flags responses that may need follow-up by care staff. Verified check-in summaries can be added to the resident care plan, so the care team and the resident or family can spot changes earlier.

Below, ScienceSoft's architects share a sample architecture of an AI-powered virtual assistant for people receiving long-term care.

AI can enhance remote patient monitoring systems used in home care and eldercare settings. AI algorithms can process the data received from the connected medical devices and smart wearables to model individual baselines for each care recipient, detect anomalies (e.g., HR spikes, gradual glucose increases), and assign individualized risk scores (of hospitalization, cognitive deterioration, etc.). When anomalies are detected, the AI system can trigger real-time alerts to caregivers, suggest diagnostic tests, and recommend care plan modifications (e.g., medication adjustments, referrals to cardiology, endocrinology, or neurology). AI-powered monitoring systems can also prioritize alerts based on severity (e.g., life-threatening vs. non-urgent) to prevent the alert fatigue of caregivers.

AI can enable fall detection systems by analyzing the real-time data received from smart wearables and wireless sensors embedded in the long-term care or home environment. The AI engine can detect critical events, such as falls or sudden immobility, and determine the need for intervention. When abnormal patterns are identified, the AI system can autonomously trigger alerts to staff or the emergency services (in case of home care). The AI engine can also analyze an individual's fall history (e.g., frequency, timing, context of falls), medical history (e.g., medications, mobility impairments, chronic conditions such as Parkinson's or diabetes), and current vitals (e.g., heart rate, gait patterns, and blood pressure) to predict the likelihood of future falls. Fall detection systems can include caregiver validation loops and real-time model retraining, improving accuracy over time and ensuring reliable alerts. AI-powered solutions can also identify potential causes or trends and offer preventive recommendations, for example, modifying the environment (removing tripping hazards, ensuring adequate lighting, or installing grab bars) or adjusting the treatment (e.g., if a fall was a side effect of a recently administered med).

The AI engine can analyze a resident's past wandering behaviors, real-time behavioral changes (e.g., increased pacing), and cognitive status (e.g., dementia stage) to predict the likelihood of wandering before it occurs. For example, if a senior resident tends to wander in the early morning when confused or agitated, AI can detect the pattern and trigger actions (e.g., alert a nurse, verbally redirect or calm a resident in case the wander management system is integrated with a virtual assistant). The AI engine can also be used to identify potential triggers of wandering incidents, such as noise or medication, assign risk scores to residents, and recommend preventive strategies like distraction, engagement, or hydration. Additionally, ML algorithms can analyze staff response time trends to support workflow improvements and enhance resident safety.

AI-powered systems can provide long-term care facilities with recommendations on optimal shift scheduling and staff allocation by considering factors like historic data on care demand (identifying high-demand periods), care recipient condition, upcoming appointments, planned family visits, employees' working hours and skills.

AI-powered community engagement platforms can connect senior residents with similar interests and become their convenient collaboration space. For example, powered by an ML-based matching engine, such platforms can help residents with similar interests find each other and make friends.

AI-powered chatbots for family engagement can automatically answer questions of family members about the health status of their senior relatives and provide the necessary information instantly, freeing up time for nurses and other caregivers.

AI-powered long-term care solutions must ensure high algorithm accuracy to generate trustworthy insights (e.g., predicting wander events) and drive effective care outcomes (e.g., timely interventions that prevent resident injuries and reduce emergency hospital visits).

Advanced AI solutions may incur high development and maintenance costs.