Scaling payer follow-up without scaling headcount

Customer snapshot

The challenge

Payer follow-up is a phone job, and the phone does not scale

Every payer enrollment application in flight needs periodic follow-up: has the payer received it, is anything missing, what is the effective date, which locations are on file. Most payers offer no dependable self-service path for these questions, so the work happens by phone. A single follow-up means navigating an IVR tree, waiting on hold, authenticating, and often being transferred before a representative pulls up the record. A meaningful share of calls end with nothing to show for them. The line drops, the representative cannot locate the provider, or the queue times out.

For a consultancy managing enrollment on behalf of roughly 3,000 providers, that phone time is the largest single labor line in the enrollment operation. Each new client group adds hundreds of enrollments, and each of those needs follow-up on a recurring cadence until it resolves. Follow-up volume grows with the book, and the only lever the firm had was headcount. Hiring credentialing specialists is slow, and a specialist who spends the afternoon on hold with a payer is a specialist who is not working privileging files, PSV, or client escalations.

Enrollment data drifts, and the first warning is a denied claim

The second problem was quieter. Once a provider is enrolled, the record starts aging. Payers reprocess rosters, move providers between locations, terminate participation when a recredentialing cycle is missed, or keep listing a provider at an address the practice left two years ago. None of this shows up in the firm’s system of record, because nothing tells the system of record it happened.

The first signal is usually a denied claim. It arrives weeks after the underlying change, gets reported by the client’s revenue cycle team, and lands on the firm as an escalation. By then the firm is investigating a problem it did not know existed, on a client relationship it is paid to protect. Multiply that across dozens of payers and thousands of providers and the pattern is clear: a consultancy can do everything right at enrollment and still be blindsided by what the payer does afterward.

The firm needed two things. A way to get payer follow-up done at volume without a proportional increase in staff, and a way to see enrollment data drift before the client’s RCM team did.

The approach

The firm runs its business on Salesforce. CredFlow was implemented as a capability layer beside it. Enrollment cases flow to CredFlow, CredFlow does the work, and results flow back into the system of record. Specialists kept their existing screens and queues. Two CredFlow workflows were put to work.

Workflow 1: AI telecaller for payer follow-up

The telecaller handles the follow-up call the way a specialist would, end to end.

Workflow 2: Enrollment data lake for drift detection

The data lake compares what the firm believes to be true in their current enrollment records against what payers publish.

The results

Payer call success rose from 65% to 85%

At the start of the engagement, 65% of telecaller calls met the success bar described above. Over the engagement period that rate climbed to 85%. The gain came from tuning the telecaller to each payer’s phone environment: mapping IVR paths for each provider services line, adjusting how long to hold and when to redial, sequencing authentication the way each payer’s representatives expect it, rephrasing status questions to match payer terminology, and timing calls to the windows when queues were shortest.

At 85%, roughly 17 of every 20 follow-up attempts return verified status data to Salesforce without a specialist touching the phone. The remaining 15% arrive with a transcript showing exactly where the call stalled, so the specialist’s rework starts from the point of failure rather than from a fresh dial.

13 minutes per successful call, moved off specialists’ desks

Each successful call averaged 13 minutes from dial to structured result. That is 13 minutes of IVR navigation, hold time, authentication, and conversation that a specialist did not spend. Across a book of 3,000 providers that time adds up quickly. The table below shows the math at a conservative monthly volume.

Volume and hourly cost are illustrative. The calculation counts only the 13 minutes of phone time on successful calls. It does not count the time specialists would have spent on the 15% of attempts that fail, which in a manual operation usually means a long hold with nothing to show for it. The real displacement is higher.

For the firm, the labor savings showed up two ways: follow-up volume it did not have to hire for, and specialist hours moved to privileging, PSV, and client work.

17% of payer enrollment records flagged for drift

As the data lake’s payer coverage expanded over the engagement, the share of the firm’s payer enrollment records flagged with a discrepancy grew to 17% of the total. Roughly one in six enrollment records had an incorrect location, an incorrect network status, or no matching payer record at all.

Reactive versus proactive

What changed for the team

Specialists spend less of the day on hold and more on the work that requires judgment: privileging files, PSV, payer escalations, and client communication. Managers work from a queue of flagged enrollment records with evidence attached rather than a stack of denial reports. And the firm’s clients hear about payer data problems from the firm, before those problems show up as lost revenue.

Why this matters for other enrollment operations

A credentialing consultancy is a demanding test for this kind of tooling. Volume is high, the provider base spans many client groups and payers, the system of record may belong to the customer rather than the vendor, and margin depends on labor efficiency. The two levers that worked here, taking the phone off specialists’ desks and finding drift before the claim cycle does, apply the same way to a medical group’s in-house enrollment team, an RCM company offering enrollment as a service, or a health system managing enrollment across employed and affiliated providers.