Multi-Agent Based Automation of Physical Therapy Home Exercise Program Delivery

Manual home exercise program (HEP) management is a significant operational bottleneck, consuming clinician time and leading to poor patient adherence, which extends recovery timelines and increases appointment frequency. A custom automation workflow addresses this by generating personalized regimens with video demonstrations directly from the PT's EMR notes, automatically delivering them to the patient portal, and initiating structured follow-up. The architecture integrates clinical reasoning, media generation, and patient engagement logic to transform a manual, inconsistent process into a scalable, outcome-driving system.

Implementation connects specialized agents to systems like Epic or WebPT for clinical data, uses retrieval-augmented generation for exercise selection, and integrates with patient engagement platforms for delivery. The workflow includes controls for clinician approval of generated regimens, exception routing for complex cases, and monitoring for adherence scoring and automated nudges. This shifts HEP from a static handout to a dynamic feedback loop, reducing administrative labor by 60% while improving patient compliance and freeing clinician capacity for higher-value care.

This workflow automates the repetitive, time-consuming process of creating and distributing personalized Home Exercise Programs (HEPs). It eliminates manual video sourcing, handout creation, and follow-up chasing, which typically consumes 15-20 minutes of clinician time per patient. The operational upside comes from freeing therapists for higher-value care, increasing patient adherence through consistent engagement, and creating billable remote therapeutic monitoring opportunities by systematically collecting patient-reported outcomes.

Implementation integrates with PT-specific EMRs like WebPT or Clinicient via FHIR/API. The Orchestrator, built on LangGraph, sequences agent tasks and manages state. The HEP Generator uses RAG against a validated exercise library. The Adherence Monitor schedules PRO surveys via integrated platforms like Phzio or patient portals. Controls include clinician review gates for initial program approval, configurable escalation thresholds for non-adherence, and full audit trails linking all agent actions to the patient record for compliance.

The primary operational bottleneck is the manual, time-consuming creation and distribution of personalized home exercise programs (HEPs), which limits therapist capacity and leads to inconsistent patient adherence. Automating this workflow with a multi-agent system directly saves 15-20 minutes per patient encounter, freeing clinicians for higher-value care. The business upside comes from improved patient outcomes through consistent, video-guided exercise, which reduces unnecessary follow-up visits and increases practice throughput and revenue per provider.

Implementation begins with a tightly scoped Phase 1, where the Generation Agent drafts HEPs but requires therapist sign-off before any patient contact, ensuring safety and building trust. Phase 2 automates delivery via integrated patient portals (e.g., integrated with Epic or Athenahealth), while Phase 3 deploys the Adherence Agent for feedback collection. This controlled rollout, governed by approval gates and integrated audit trails in the EMR, allows for iterative refinement of exercise logic and exception handling before full autonomy, mitigating clinical and operational risk.

Operationalizing HEP automation demands architectural controls that enforce clinical oversight without creating bottlenecks. The workflow must integrate with the PT EMR (e.g., WebPT, Clinicient) to pull prescribed regimens and patient profiles. A primary governance agent validates all AI-generated exercise content against clinical guidelines and flags high-risk movements for therapist review before release. This pre-flight check, combined with immutable audit logging of all agent decisions, creates the defensible safety layer required for regulated care delivery, turning automation from a risk into a compliance asset.

Rollout should be phased, starting with low-complexity, stable post-op cohorts to validate system performance and patient engagement. Monitoring is critical: a dedicated observability layer tracks exercise video views, patient-reported pain scores, and completion rates, feeding into the adherence dashboard. Exceptions—like reported increased pain or missed sessions—are automatically routed to the assigned therapist via the EMR's tasking system. This closed-loop control architecture ensures the workflow augments care safely, driving the intended outcome of reduced appointment frequency while maintaining therapeutic alliance and clinical accountability.

This workflow automates the creation, delivery, and monitoring of home exercise programs (HEPs), a high-volume, repetitive task that consumes 15-20 minutes of clinician time per patient. The operational upside comes from extending care between visits, improving patient adherence and outcomes, and freeing up therapist capacity for higher-value activities. It requires integration with the PT EMR (e.g., WebPT, Clinicient) for patient data and prescribed protocols, and a patient engagement system for multimedia delivery and feedback collection.

Implementation begins by establishing a real-time feed from the EMR for discharge events and protocol details. The generation agent uses a specialized LLM, fine-tuned on PT exercise libraries, to create a patient-specific regimen considering limitations and goals. Video agents render demonstrations using avatar libraries. The adherence monitor parses patient-reported feedback via chat or forms, scoring compliance and routing concerns to therapists via in-EMR alerts or dashboards. Governance requires clinician review gates for initial program approval and configurable escalation rules for non-adherence.