FloQast vs. AuditBoard for Multi-Clinic Physical Therapy Networks

Say a clinic sees 70 visits in a week across four payers, each with its own contracted rate per CPT code billed. The claims submitted that week should tie to a specific expected reimbursement based on those contracted rates, not just to whatever eventually gets paid. When actual payments come in over the following weeks, reconciling them against the expected amount, by payer, surfaces underpayments and denials individually instead of only as a vague gap between billed and collected revenue at month end.

A denied claim that gets resubmitted and forgotten about in the accounting system means the original revenue estimate never gets corrected if the resubmission is denied again or paid at a different rate. Reconcile denials as their own account: track what was originally expected, what was actually collected after resubmission, and write off or escalate anything aged past your clinic's typical resolution window. A denials account that nobody reviews tends to accumulate small write-offs that never get formally recognized.

Payer contracts renegotiate periodically, and a rate change that isn't reflected in the reconciliation's expected-payment calculation means every visit billed under the old assumption looks like an underpayment that isn't actually one. Keep the contracted rate schedule current and reconcile it against a sample of recent payments quarterly, separate from the monthly claims reconciliation, specifically to catch a rate change the clinic wasn't notified about before it distorts every reconciliation downstream.

Visit-to-payment reconciliations, denial tracking, and rate schedule reviews are recurring work that repeats identically at every clinic, which is exactly what FloQast's checklist model handles: a named preparer and reviewer per clinic, a variance that stays open until it's explained, and a rollup that shows a network controller which clinics closed clean without rebuilding the picture from separate payer reports.

As a PT network grows past a handful of clinics or brings on outside investment, lenders and investors increasingly want documented evidence that billing and collections reviews actually happen at each clinic, not just that the network-level revenue number ties out. AuditBoard holds that evidence: who reviewed each clinic's denial queue, on what cadence, and whether the biller submitting claims is separate from whoever posts the resulting payments.

• If visit-to-payment reconciliation and denial tracking are the recurring mess at close, start with FloQast.
• If a lender, investor, or acquirer's diligence team has started asking for documented clinic-level review evidence, bring in AuditBoard.
• If contracted rate schedules are out of date at several clinics, fix that directly with your billing team first, since neither platform substitutes for the underlying rate data being accurate. MeetMyCFO's AI CFO, Frank, can flag which clinic's denial rate has moved the wrong direction before the network average hides it.

Many payers require prior authorization for a set number of visits, and treatment delivered past that authorized count without a renewal in hand often gets denied outright rather than partially paid. Track authorized visit counts against actual visits delivered by patient, and flag any patient approaching their authorization limit before the clinic delivers a session that risks going unpaid entirely. A clinic that doesn't track this proactively tends to discover the problem only when the denial arrives weeks later, by which point the unauthorized sessions have already been delivered and the revenue is much harder to recover.

Say one payer's contracted rate should generate roughly the same average reimbursement per visit each month, but the clinic's actual collections from that payer drift lower over three consecutive months. Reconciled by payer, that drift is visible immediately and worth investigating, whether it's a rate change that wasn't communicated, a coding pattern that shifted, or a denial rate that's crept up without anyone noticing. Left inside a blended total-revenue number across all payers, the same drift is invisible until it's large enough to affect the bottom line noticeably, at which point diagnosing the specific cause takes considerably longer than if it had been caught payer by payer from the start.