Learning Points
Learning Points are AI-generated insights about your billing patterns. SnapBill analyzes your claims history to identify recurring errors, missed opportunities, and areas for improvement.
How It Works
SnapBill continuously reviews your claim data — especially rejected claims — and identifies patterns that can help you bill more accurately. These insights are surfaced as Learning Points on a dedicated page.
Viewing Learning Points
Navigate to Tools > Learning Points in the sidebar. Each learning point includes:
- Title — a summary of the pattern or issue
- Severity — Critical, High, Medium, or Low
- Pattern type — what kind of issue was found (error codes, code combinations, fee mismatches, SOB violations)
- Frequency — how often this pattern occurs in your claims
- Estimated impact — the potential revenue loss from this error pattern
- Affected claims — links to the specific claims where this issue occurred
- Recommendation — actionable advice on how to avoid this error
Severity Levels
| Level | Meaning |
|---|---|
| Critical | Significant revenue loss or compliance risk — address immediately |
| High | Recurring issue affecting multiple claims |
| Medium | Occasional error worth being aware of |
| Low | Minor optimization opportunity |
Example Learning Points
- “Code A007 is being rejected 15% of the time due to missing diagnostic codes — always include a diagnostic code when billing consultations”
- “You’re billing A003 for patients over 70 — consider A004 (geriatric assessment) which has a higher fee and is more appropriate for this age group”
- “Claims at facility 1234 are frequently rejected — verify the facility master number is correct”
Filtering
Filter learning points by:
- Severity — focus on critical and high items first
- Pattern type — error codes, code combinations, etc.
- Billing code — see insights for a specific code
Dismissing Learning Points
If a learning point is no longer relevant or you’ve already addressed it, click Dismiss to remove it from your list. Dismissed insights won’t reappear unless the same pattern resurfaces.