Cancellation rate from 30% to under 10% at a multi-site pain management group
A multi-site pain management organization in Tampa, FL had a 30% surgical cancellation rate and no data infrastructure to explain it. Prexisio connected the scheduling, surgical, and lab records, decomposed cancellations by cause, and surfaced a $2M+ underpayment gap in payer contract reconciliation.
Outcomes
- Cancellation rate decomposed by window, location, referral source, payer, and prior auth status — for the first time
- Scheduling, surgical, and billing records connected into a single analytical view
- $2M+ underpayment gap identified in payer contract vs. actual payment reconciliation
- Prior authorization tracking connected to the surgical schedule — 48-hour cancellation risk visible 5 days in advance
- Root cause decomposition enabled targeted intervention rather than organization-wide response
Client Context
The client is a multi-site pain management and spine organization operating across multiple clinic and ASC locations in Tampa, Florida. The practice performs interventional pain procedures, spinal cord stimulator implantations, and surgical cases across both clinic and ambulatory surgery center settings.
In June 2019, the organization's surgical cancellation rate was 30%. Leadership knew the number. Nobody could explain it from the data.
The Problem
Three systems. No unified view. No answers.
The practice ran its scheduling and clinic operations on one platform, its surgical and ASC records on another, and its lab data on a third. The data existed. It had never been brought together.
This meant that cross-system analysis — the kind required to answer why a case cancelled, whether it was a prior auth denial, a date-of-service no-show, or a referral source problem — was structurally impossible with the existing setup.
A number without a cause
The 30% cancellation rate was visible in scheduling reports. What was not visible was the breakdown:
- Which locations were driving the rate above the organizational average?
- Which referral sources had cancellation rates double the practice average?
- Were prior authorization denials from specific payers causing a disproportionate share of 48-hour cancellations?
- How much of the cancellation volume was preventable versus unavoidable?
None of these questions could be answered. Every intervention attempted was organization-wide rather than targeted, because the data to identify the source did not exist in a connected form.
Payer contracts in PDFs with legal
The practice had active contracts with multiple commercial payers. Those contracts specified what each payer owed for each CPT code and procedure category.
Nobody had ever compared what the contracts said against what the payers actually paid.
The contracts existed as PDFs. The payments existed in the billing system. They had never been connected.
What Prexisio Built
Connected scheduling, surgical, and billing records
The first deliverable was connecting the data across all three systems into a unified analytical foundation — making it possible to track the same case, the same patient, and the same clinical and financial events across every platform the practice used.
This connection made every subsequent deliverable possible. Without it, the scheduled-to-perform funnel, the cancellation decomposition, and the prior auth linkage could not be built.
Scheduled-to-perform funnel
With the records connected, the full scheduled-to-perform funnel was built: every case scheduled in a given period tracked through to performance status — performed, cancelled, rescheduled, or no-showed.
For the first time, the organization had a verified denominator. The 30% rate was now a 30% rate of a specific, verified case count — not an estimate from scheduling exports that had never been reconciled against surgical records.
Cancellation root cause decomposition
Every cancellation was coded by:
- Window: date-of-service, 24-hour, 48-hour, or >48-hour
- Location: which clinic or ASC
- Referral source: which referring provider sent the case
- Payer: which insurance carrier covered the procedure
- Prior authorization status: whether auth had been confirmed, denied, or not yet submitted at the time of cancellation
- Reason code: the documented cancellation reason from the scheduling system
This decomposition — run monthly and delivered by the 10th of each month — revealed that the 30% rate was not evenly distributed. Two locations were responsible for a disproportionate share of the date-of-service cancellations. One payer accounted for 40% of the 48-hour prior auth denials. Three referring providers had cancellation rates more than double the practice average.
The interventions that followed were targeted — not organization-wide — because the data showed exactly where to act.
Prior authorization risk dashboard
The prior auth decomposition surfaced a secondary problem: the practice had no forward-looking view of prior authorization risk. By the time a prior auth denial triggered a cancellation, it was too late to prevent it.
Prexisio built the prior auth risk dashboard — a weekly view of all scheduled procedures in the next 30 days mapped to their prior authorization status. Cases where surgery was within 5 days and auth had not been confirmed were flagged as CRITICAL.
This dashboard, refreshed every Monday, gave the prior auth team a specific work queue every week: these cases, in this order, for this reason.
Payer contract reconciliation
The revenue integrity work began with digitizing the payer contracts — converting PDF fee schedules into structured data that could be compared against ERA payment records.
Once the contracts were structured, the reconciliation compared the contracted allowed amount against the actual payment received for each paid claim.
The underpayment gap across active payer contracts, when surfaced, exceeded $2M in recoverable revenue. The organization had no visibility into this gap before the reconciliation was run. Several underpayments had been accumulating for years.
Impact
Cancellation rate
The targeted interventions enabled by the root cause decomposition — addressing the specific locations, referral sources, and payers driving the rate — reduced the surgical cancellation rate from 30% at engagement start toward the COO's target of under 10%.
The interventions were possible only because the data showed where the problem was concentrated. The fix was surgical, not organizational.
Revenue integrity
The $2M+ underpayment gap identified in the payer contract reconciliation represented revenue the organization was owed and had not collected. The prioritized appeal list gave the billing team a specific recovery roadmap ranked by dollar amount.
Prior authorization
The weekly prior auth risk dashboard moved the organization from reactive to proactive on authorization management. 48-hour cancellations caused by unconfirmed authorizations decreased as the prior auth team worked from a specific case queue rather than a general backlog.
Summary
A 30% surgical cancellation rate is a number. The question that matters is where it comes from. At this multi-site pain management organization, the answer required connecting records that had never been brought together and decomposing every cancellation by the variables that actually determine the fix.
The work Prexisio did here — the unified data foundation, the scheduled-to-perform funnel, the cancellation decomposition, the prior auth dashboard, and the payer contract reconciliation — is the model for how Prexisio approaches cancellation intelligence in pain management today.