AI Medicare Rules Engine for Healthcare Admin
AI assistant trained on Medicare rules to answer billing and compliance queries in real time. Reduced billing errors and eliminated hours of manual MBS code research.
Multi-location GP network — billing and administration
A GP network with 5 clinics and 18 practitioners. Their billing team handled Medicare claims across all locations — processing claims, resolving rejections, and answering practitioner questions about MBS item numbers, bulk billing rules, and patient eligibility.
Medicare billing rules are complex and change regularly. The team relied on the MBS online search tool, printed quick-reference guides (often outdated), and institutional knowledge held by the two most experienced billing staff. When those two were away, errors spiked.
What needed to change
Medicare rules are complex and constantly changing. Over 5,800 MBS item numbers, each with specific rules about who can bill them, when, in what combination, and at what rate. Changes happen quarterly and sometimes more frequently.
Knowledge was concentrated in two people. The senior billing staff knew the rules from years of experience. When they were on leave or busy, the rest of the team made errors — wrong item numbers, missed co-claiming restrictions, rejected claims.
Searching for answers was painfully slow. The MBS online tool is functional but not intuitive. Finding the answer to a specific billing question often required checking multiple item descriptions, cross-referencing explanatory notes, and reading through guidelines. Even experienced staff could spend 15–20 minutes per complex query.
What we built
An AI assistant trained on the full MBS schedule, explanatory notes and Medicare guidelines. Staff ask billing questions in plain English and get accurate, sourced answers instantly.
Knowledge Base
Full MBS schedule, explanatory notes, Medicare guidelines and DVA billing rules ingested and indexed. Updated automatically when Medicare publishes changes.
Natural Language Search
Staff type questions in plain English — "Can I co-bill 23 and 36 for the same patient on the same day?" — and get a clear yes/no answer with the specific rule cited.
Source Citations
Every answer links to the specific MBS item, explanatory note or guideline section. Staff can verify and learn the underlying rules, not just get answers.
Auto-Updates
When Medicare publishes MBS updates, the knowledge base refreshes automatically. Staff are notified of changes relevant to their most commonly billed items.
How it works
Billing staff types a question
Natural language query in the search bar — no need to know MBS item numbers upfront. Questions can be about item eligibility, co-claiming rules, patient criteria or bulk billing entitlements.
AI retrieves relevant rules
The RAG engine searches the indexed MBS schedule and guidelines for the most relevant sections. Retrieves specific item descriptions, notes and cross-references.
Clear answer generated
GPT-4 generates a concise answer based on the retrieved Medicare rules. Constrained to source material — no hallucination.
Citations link to source documents
Answer includes links to the specific MBS items and guideline sections. Staff can click through to read the full rule text.
Feedback improves accuracy
Staff can flag incorrect or incomplete answers. Corrections inform retrieval tuning and prompt refinement.
Measurable outcomes
My team used to come to me with billing questions 30 times a day. Now they ask the AI first and it is right almost every time. When I am on leave, errors do not spike anymore. That is the real win.
How we delivered it
Data Ingestion
2 weeksIngested the full MBS schedule (5,800+ items), explanatory notes, Medicare guidelines and DVA billing rules. Processed, chunked and embedded for semantic search.
RAG Engine Build
3 weeksBuilt the retrieval and generation pipeline. Optimised chunking and retrieval for billing-specific query patterns. Implemented citation linking and confidence scoring.
Interface & Integration
2 weeksBuilt the search interface — clean, fast, designed for busy billing staff. Added browser extension for quick access during claims processing.
Testing & Refinement
2 weeksTested with real billing scenarios across all 5 clinics. Validated accuracy against senior billing staff knowledge. Refined for edge cases and complex co-claiming queries.
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