Behind the 70% Ticket Reduction: Our Framework

The 70% support ticket reduction we've delivered for multi-clinic networks isn't the result of a single clever chatbot — it's a repeatable, 3-step framework that works regardless of clinic size or specialty.

Step 1: Unify the knowledge base

Before any AI can answer questions accurately, it needs a single source of truth. We start by connecting the clinic network's EHR, ticketing system, and existing FAQs into one knowledge base, so every clinic in the network is working from the same, up-to-date information — no more conflicting answers between locations.

Step 2: Train on real historical tickets

Generic chatbot scripts fail in healthcare because every clinic's patient base asks slightly different questions. We train the AI on the network's own historical support tickets, so it learns the actual phrasing, context, and edge cases specific to that clinic — not a generic script pulled from a template.

Step 3: Escalate with context, not from scratch

1. The AI resolves routine questions instantly: appointment changes, insurance checks, refill status.
2. Anything clinical, sensitive, or outside its confidence threshold is escalated to a human.
3. The human agent receives full conversation history and sentiment analysis — no "start from zero" moment for the patient.

Why this order matters

Skipping step 1 (unifying the knowledge base) is the most common reason AI support projects underperform — the model ends up giving inconsistent answers across clinics because it's working from fragmented data. Getting the data foundation right first is what makes the 70% deflection rate achievable and repeatable.

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