A financial model built on the fee schedule looks precise: multiply expected volume by the contracted rate, get a revenue number. That number is usually wrong, because it treats the fee schedule as the revenue a facility actually collects, instead of the ceiling on what it might collect if every claim paid in full and on time.
- The fee schedule is a ceiling, not a forecast — actual collections run below it once denials, underpayments, and delays are accounted for.
- A collections-based forecast starts from what actually gets paid, historically, rather than what the contract says should get paid.
- Expansion models need a payer-specific credentialing runway, not a blended average, because the slowest payer determines the real cash gap.
- All three corrections point the same direction — model what payers actually do, not what the paperwork says they should do.
Each of the three corrections below changes a different input, and none of them is a spreadsheet formatting choice. They are what separates behavioral health financial modeling from a general FP&A exercise applied to a specialty it was never built for.
Start with the core mistake. the fee-schedule myth covers why treating the contracted rate as expected revenue overstates what a facility will actually collect, sometimes substantially.
The gap between the fee schedule and actual collections isn’t a rounding error. AHA’s own cost-of-caring data reflects how wide that gap can run industry-wide, well before behavioral health’s added layers of denials and underpayment are factored in.
Denials, underpayments, and payment delays all sit between the contracted rate and the dollars that actually land, and a model that ignores that gap is modeling a number that doesn’t exist.
A revenue projection built from a facility’s actual historical collection rates by payer, rather than from contracted fee schedules, accounting for denials, underpayments, and payment timing that the fee schedule alone doesn’t capture.
Fixing that gap means changing what the model is built from. Building a collections-based forecast covers how to model from actual historical collection patterns by payer instead of the contracted rate, producing a number that reflects what a facility has actually been paid, not what it’s owed.
This matters most for planning decisions that assume a specific cash position. A model built on the fee schedule can show a facility comfortably profitable while its actual collections tell a very different story. Replacing the fee-schedule assumption means sourcing actual allowed amounts by payer from adjudicated claims rather than from the contract’s stated ceiling.
A fee-schedule model assumes payers pay in full and on time. A collections-based model assumes they behave the way they actually have historically. The second assumption is harder to build, since it requires real payment data instead of a contract, but it’s the only one that produces a number a facility can actually plan around.
The same principle applies when modeling growth. Modeling expansion and reimbursement runway covers why a new location’s break-even model should be set by the slowest payer’s credentialing timeline, not a blended average across payers.
A new location can’t bill any given payer until that payer’s own credentialing process finishes. Averaging timelines across payers hides the one that actually determines how long the cash runway needs to last.
Collections-based, not fee-schedule-based. Payer-specific, not blended. That’s the throughline across all three pieces, and it’s what separates a financial model that reflects reality from one that just reflects the contract.
Why is the fee schedule an unreliable basis for a revenue forecast?
Because it represents the contracted ceiling, not what actually gets collected. Denials, underpayments, and payment delays all reduce actual collections below the fee-schedule figure.
What does a collections-based forecast use instead of the fee schedule?
A facility’s actual historical collection rates by payer, which account for denials and underpayments the contracted rate alone doesn’t capture.
Why shouldn't an expansion model average credentialing timelines across payers?
Because a new location can’t bill a payer until that specific payer finishes credentialing. The slowest payer, not the average, actually determines the cash runway required.
Is a collections-based model harder to build than a fee-schedule model?
Yes, since it requires actual historical payment data rather than just the contracted rate, but it produces a number that reflects real payer behavior instead of assumed full payment.
- Forecasts built from actual collection rates, not contracted ceilings
- Expansion runway sized to the slowest payer, not a blended average
