Automated Patient Inquiry Triage and Resolution Assistant

Healthcare contact centers and patient support teams face high volumes of repetitive questions (appointments, billing, medication instructions, test results) and limited staff, causing long wait times, inconsistent answers, and backlogs that affect patient experience and clinical follow-up.

Deploy a hybrid AI assistant that combines intent classification, retrieval-augmented generation over curated clinical and policy content, and human-in-the-loop escalation for sensitive or low-confidence cases. The system enforces PHI handling rules, logs decisions for audit, and continuously improves from agent corrections.

• Use a small to medium LLM with RAG over an approved, versioned knowledge base (clinical FAQs, billing policies, scripts) and dynamic connectors to scheduling/EHR systems for non-sensitive lookups.
• Implement a triage pipeline: NLU intent + entity extraction -> rule-based safety checks -> AI-drafted reply with confidence score and suggested actions (schedule, refer, escalate) presented to agent or auto-send when above a high-confidence threshold.
• Human-in-the-loop and escalation rules for low-confidence, clinical advice, or PHI-heavy requests; capture agent edits for supervised fine-tuning on de-identified logs.
• Governance: role-based access to PHI, audit trails, consent logging, periodic red-team testing of hallucination and safety scenarios, and retention policies aligned with HIPAA and internal compliance.

Illustrative results: teams typically see a 20-40% reduction in average handle time and a 15-35% drop in cost per contact by automating routine inquiries and draft responses; first-contact resolution can improve by ~10-25% and escalation volume to clinical staff can fall 30-50% when rules and human review are well tuned. A mid-market provider with steady inquiry volume can often realize payback within 6-12 months depending on automation scope and integration complexity.