Vet No-Show Billing Decision Tree
Vet No-Show Billing Decision Tree
Determines whether to waive, charge, reschedule, or escalate a missed-appointment fee for a veterinary clinic by reviewing the calendar event, client communications, invoice history, and clinic policy, producing a clear pre-contact recommendation with rationale.
What the agent does
These are the instructions your agent follows. It asks you for what it needs, then does the work in chat.
Goal
Generate a clear, policy-based decision package—waive, charge, reschedule only, or escalate—for a missed veterinary appointment, complete with fee details, rationale, evidence, and staff next steps, before any client contact.
Inputs to gather
- Appointment details (date, time, type, provider/resource, location, pet, client) — anchors all record look-ups and policy rules.
- Appointment calendar logs (booking, reminders sent/delivered, confirmations, cancellations, arrival/no-show status) — proves timing and classification.
- Client communications from the last 30 days (email, SMS, call notes) — reveals cancellations, emergencies, or disputes.
- Invoice & payment history (deposits, past no-show charges/waivers, membership/plan status, disputes, balance) — affects fee calculation and repeat-offense checks.
- Clinic policy references (fee schedule, late-cancel windows, waiver criteria, repeat thresholds, escalation rules, deposit policy) — authoritative basis for decisions.
- Weather or closure records for the appointment date (if weather exemptions exist) — confirms safety-closure waivers.
Before doing any work, ask the user for these inputs in ONE message. Skip anything they already provided. If they tell you to decide, choose sensible defaults and say what you chose.
Workflow
- Gather and review all supplied records; list any missing or conflicting data.
- Classify the event outcome
a. Identify as no-show, late-cancel (<48 h, <24 h, <2 h), or clinic-cancel.
b. Flag “ambiguous-facts” if logs conflict or proof is absent. - Hard-waive screen
• If clinic_error, system_outage, or documented weather closure applies → set Decision = WAIVE; Reason_code accordingly; proceed to step 7. - Soft-waive screen
• If first_time_courtesy, documented_emergency, or compassionate_exception qualifies → set Decision = WAIVE (or reduced per policy); Reason_code accordingly; proceed to step 7. - Standard fee rules (when no waiver triggered)
a. Determine appointment category and correct late-cancel tier.
b. Compute fee or deposit forfeiture per policy and membership terms.
c. Set Decision = CHARGE with Reason_code (late_cancel_<48h, late_cancel_<24h, no_show, surgery_block_forfeit). - Escalation check
• If repeat_offense threshold met, high_dollar amount, ambiguous_facts, special_terms, hardship, or conduct_risk exists → override Decision = ESCALATE; Reason_code accordingly.
• If policy allows fee-free reschedule without waiver, optionally set Decision = RESCHEDULE_ONLY with supporting rationale. - Incomplete data safeguard
• If any core record missing → Decision = ESCALATE; Reason_code = INCOMPLETE_DATA; list data required. - Assemble the decision package
- action (WAIVE | CHARGE | RESCHEDULE_ONLY | ESCALATE)
- fee_amount (number, currency, line-item code, tax flag)
- reason_code
- rationale (1–3 sentences linking evidence to policy)
- evidence list (timestamps/IDs, message excerpts, invoice IDs, policy citations)
- front_desk_next_steps
- client_message_template (polite, editable)
- account_updates (courtesy counters, flags)
- reviewer_owner (if escalated)
- Present the decision package to the user. For escalations or missing data, clearly state what follow-up is required.
- Log summary instructions: suggest saving the package to the client record and, if escalated, routing to the designated reviewer.
For lengthy or uncertain cases, pause after step 6 and confirm with the user before compiling the final package.
Output
A structured decision package (plain text or JSON) containing: action, fee_amount with currency and line-item code, reason_code, 1–3 sentence rationale, evidence list, front_desk_next_steps, client_message_template, account_updates, and reviewer_owner if escalation applies.
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Key Benefits
Discover how our intelligent prompt chain enhances your workflow
Consistent, Auditable Decisions
The decision tree enforces a repeatable, documented process (collect records → validate → apply waiver/fee rules → produce decision package). By requiring evidence lists, rationale sentences, and account logging, staff produce a clear audit trail. This helps users learn how policy maps to outcomes and reduces variability between staff — each decision becomes a teachable example tied to specific timestamps, messages, and policy citations.
Faster, Lower‑Error Decision Making for New and Experienced Staff
The stepwise instructions (identify appointment, gather logs, classify event, screen hard/soft waives, apply fee rules) reduce cognitive load and guide users through complex cases. Following the structured checklist accelerates decisions, minimizes missed checks (timezones, moved appointments, reminder delivery), and trains staff by repetition: novices internalize the logical order and experienced staff work faster with fewer mistakes.
Evidence‑Based, Empathetic Client Communication
Producing a concise rationale plus a pre‑draft client message template (with placeholders and empathy guidance) teaches staff how to translate policy and evidence into non‑adversarial conversations. Users learn to cite the precise facts that informed the decision and to use tone/phrasing that reduces conflict and dispute risk, improving customer experience while preserving clinic policy.
Clear Escalation Triggers and Learning Opportunities
Explicit escalation criteria (repeat offenses, high‑dollar impact, ambiguous or missing data, hardship or VIP flags) and the requirement to compile a decision package for reviewers create consistent handoffs and coaching moments. Staff learn threshold boundaries, what documentation is required, and when to seek manager input — converting ambiguous or complex incidents into structured learning cases for future policy adherence.
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