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Hospital OperationsGenerated accuracy model review: 2026-05-06
Provider Panel Size Calculator accuracy and testing
This page documents how Calwarden builds trust for this calculator: source notes, regression-style default tests, limitations, assumptions, unit handling, rounding, and visible change history.
Formula source
- Panel size = annual visit capacity / visits per patient per year, reduced by access buffer.
Test cases
Default input regression check
Generated from the checked-in default inputs and calculate function.
Inputs
- Provider FTE: 1
- Visits per Day: 18
- Clinic Days per Year: 220
- Visits per Patient per Year: 2.6
- Access Buffer: 10%
Expected outputs
- Recommended Panel Size: 1,370.77
- Annual Visit Capacity: 3,960
- Panel Before Buffer: 1,523.08
Known limitations
- This page documents the checked-in calculator logic and does not certify results for every real-world edge case.
- Use the result as a planning estimate unless the calculator page states a stricter purpose.
Assumptions
- The calculators use simple transparent formulas rather than vendor-specific analytics models.
- Inputs are processed locally in the browser and are not stored by Calwarden.
- This is operations planning math, not medical, legal, staffing, billing, or financial advice.
Unit handling
- Provider FTE accepts numeric values.
- Visits per Day accepts numeric values.
- Clinic Days per Year accepts numeric values.
- Visits per Patient per Year accepts numeric values.
- Access Buffer accepts values in %.
Rounding rules
- Number displays use up to 2 decimal places in the calculator UI.
- Input precision follows configured step values where provided.
Version history
1.0.0 - 2026-05-06
Generated accuracy and testing page from the current provider-panel-size-calculator calculator definition.
Change log
2026-05-06
Published default regression test documentation and metadata fallbacks for Provider Panel Size Calculator.
Calculator transparency
Accuracy pages are generated from checked-in calculator definitions and can be upgraded with curated review metadata over time.