AI Agentic Workflow for Detecting Activities of Daily Living (ADL) Decline

This workflow automates the detection of subtle but critical declines in Activities of Daily Living (ADL)—like cooking, bathing, and mobility—by analyzing patterns from non-invasive ambient sensors on appliances, cabinets, and motion detectors. It replaces manual, infrequent wellness checks with continuous, passive monitoring, directly targeting the operational bottleneck of reactive care. The business value is clear: earlier intervention reduces avoidable hospitalizations and delays costly transitions to assisted living, creating significant savings for families and payers while improving quality of life.

Implementation requires a multi-agent architecture built on frameworks like LangGraph, with agents specialized for sensor fusion, temporal pattern recognition, and clinical guideline mapping. Data flows from IoT hubs (e.g., SmartThings, proprietary systems) through a secure pipeline to a central orchestrator. Critical constraints include managing false positives through confidence thresholds, integrating with occupational therapy scheduling systems, and ensuring all alerts include explainable rationale for clinician review. The rollout must be sequenced by home, with continuous model retraining on new resident baselines to maintain accuracy.

This workflow automates the detection of subtle changes in Activities of Daily Living (ADL) by establishing behavioral baselines from ambient sensors on appliances, cabinets, and motion detectors. It eliminates manual review of raw telemetry, converting passive data into actionable clinical insights. The operational upside comes from preventing costly adverse events—like falls or malnutrition—through early OT referrals, reducing avoidable hospitalizations and extending safe independent living. Savings are realized in lower acute care costs and reduced caregiver monitoring burden.

Implementation requires integrating with IoT platforms like SmartThings or proprietary sensor gateways, using a framework like LangGraph for agent orchestration. Data flows through a time-series database before being processed by specialized agents for pattern recognition. Critical controls include configurable deviation thresholds, mandatory human review for high-severity flags, and audit trails for all automated decisions. The system must handle poor data quality from offline sensors and route exceptions to technical support queues. Rollout is typically phased by care facility wing or individual home to validate model accuracy before full deployment.

Phase 1 establishes the data backbone, ingesting raw events from ambient sensors (SmartThings, Aqara) and wearables into a time-series database. We deploy lightweight agents to establish individual baselines for kitchen, bathroom, and mobility patterns, validating data quality and sensor coverage. This foundational layer, integrated via HL7/FHIR with EHRs like Epic, creates the single source of truth required for reliable anomaly detection, setting the stage for algorithmic validation.

Phase 2 pilots the full agentic orchestration using LangGraph, where detection agents score deviations against baselines and route high-confidence alerts to a human-in-the-loop dashboard for clinician review. We validate the workflow's precision and recall in a controlled assisted living setting, tuning confidence thresholds and exception routing. Final phase scales the validated architecture, automating caregiver notifications via Twilio and creating audit trails in ServiceNow, ensuring the system reduces manual checks while preserving safety governance.

Operationalizing ADL monitoring demands a control plane that governs data quality, alert routing, and human-in-the-loop review. The workflow must integrate with clinical systems like Epic for patient context and include approval gates for high-risk alerts before caregiver notification. Implementation begins with a rules-based baseline, using sensor data from smart plugs and motion detectors to establish patterns before layering in ML anomaly detection. This phased approach validates signal reliability and builds clinical trust, ensuring the system reduces false alarms and focuses interventions where they create the most value in preventing hospitalizations.

Rollout should be sequenced by sensor type and resident acuity, starting with non-critical activities like appliance usage before advancing to mobility and bathroom patterns. Each phase requires clear performance KPIs (e.g., precision/recall of alerts) and defined escalation paths to occupational therapists or nursing staff. The final architecture must include comprehensive audit trails for all agent decisions and model inferences to satisfy healthcare compliance (HIPAA) and provide defensible evidence for care plan adjustments, turning raw sensor data into a trusted clinical decision-support layer.