The problem was never just the maze. It was the data we carried through it.
Provider credentialing delays aren’t caused by too many regulatory bodies; they’re caused by manually entered data that’s wrong from the moment it’s keyed in. Axuall’s clinician data network exists to fix that root cause, not just to move the paperwork around faster.
A recent Forbes Technology Council piece, “The Provider Credentialing Maze: Are We Protecting Patients or Slowing Care?” by John Bou, has been getting deserved attention. It captures the scale of the problem well: per Forbes, a single credentialing application can require upward of 140 core data elements, the average physician holds contracts with roughly 19 payers at a time, some 70 state medical and licensing boards apply their own criteria, and credentialing timelines routinely stretch to three or four months. There’s one thing we know to be true: A clinician’s credentials will be checked and rechecked hundreds of times over a career.
The article is right that this duplication of work is a structural problem that the industry needs to correct. But duplication is the symptom. The disease is what all that duplicated work is operating on: manually entered data that is inherently flawed, riddled with errors, omissions, and stale entries from the moment it is keyed in.
The maze is a symptom. Manual data is the disease.
Consider what actually happens inside the maze. A clinician types their history into an application. A coordinator may then have to re-key it into a credentialing system. A payer transcribes it again for enrollment. A directory team copies it once more. At every step, humans transcribe documents, self-reported forms, and aging database records by hand. Every keystroke is a chance to introduce an error, and every form is a chance to omit something. Then, verification teams spend months trying to catch the mistakes that manual entry guaranteed in the first place.
Why Doesn’t Centralizing Credentialing Data Fix the Problem?
This is why centralizing the process, on its own, has never solved it. A central repository of manually entered data is just a bigger filing cabinet of the same errors and omissions, distributed more efficiently. You cannot fix a data quality problem with a workflow tool. The duplicated work is only necessary because no one trusts the underlying data. Remove the flaws from the data, and most of the duplication loses its reason to exist.
Real-world primary source data changes the equation
Here is what has changed and what makes this moment different from every previous attempt at credentialing reform: real-world primary-source data now exists at scale. Licensure boards, DEA registrations, certifications, sanctions, education, training, and practice data can be drawn directly from the sources that originate them, rather than transcribed from what a person remembers to write on a form. This is the foundation Axuall’s clinician data network is built on.
Three capabilities turn that raw material into working infrastructure:
Self-healing data connectors maintain live links to primary sources and repair themselves when those sources change formats, fields, or interfaces. The data pipeline does not silently break and start serving stale records; it adapts and keeps flowing.
Automated consolidation resolves the many fragmented records for every clinician into a single, coherent, longitudinal record, reconciling conflicts with the primary source rather than asking a human to guess which entry is correct.
AI insights rest on data that is actually true. That distinction matters. AI applied to manually entered data just produces wrong answers faster. AI applied to consolidated primary-source data can flag risks, predict attrition, surface network gaps, and answer workforce questions that were previously unanswerable.
Beyond credentialing: engagement, directories, and access
Once clinician data is drawn from primary sources and kept continuously up to date, credentialing becomes dramatically faster. But stopping there would undersell what is happening. The same data foundation transforms everything downstream that depends on knowing, accurately and right now, who a provider is and what they can do.
Provider engagement improves because clinicians are no longer asked to fill out the same forms for the dozenth time; now they’re just asked to verify what the system has already found on their behalf, from trusted sources. Provider directories, a chronic and well-documented source of accuracy problems across the industry, stay current automatically rather than decaying between manual audits. And accurate, real-time directories are the raw material for precision scheduling and genuine access improvements: patients matched to the right clinician with the right qualifications and availability, the first time.
This future is not aspirational. It’s here.
Axuall leads this shift, with the first widespread, proven clinician data network in production across millions of providers and many of the largest healthcare organizations in the United States. Health systems are not piloting a concept. They are collecting credential proof in minutes rather than weeks, engaging clinicians without duplicative paperwork, and running workforce intelligence using data drawn directly from the source.
The Forbes piece asks whether credentialing protects patients or slows care. The way to do the first without the second is not to wrap more processes around flawed data. Axuall replaces manual credentialing data entry with continuously verified, primary-source data cutting verification time from months to minutes. The maze has a way out, and the industry is already walking through it.
To learn how Axuall’s clinician data network powers credentialing, workforce intelligence, and provider data operations, request a meeting.
FAQ
What causes provider credentialing delays?
Manual data entry, not the number of regulatory bodies involved. Every time a clinician’s information is re-keyed by hand (on an application, in a credentialing system, during payer enrollment) there’s a new chance for errors and omissions that verification teams then have to catch, which is what stretches timelines to three or four months.
Does centralizing credentialing data solve the problem?
Not on its own. A central database built from manually entered records still contains the same errors, just in one place. The fix is sourcing data directly from primary sources: licensure boards, DEA registrations, certifications, so the data is accurate before it’s centralized.
How does Axuall reduce credentialing time?
Axuall’s clinician data network pulls verified data directly from primary sources through self-healing data connectors, consolidates fragmented records into a single longitudinal profile per clinician, and applies AI insights to data that’s already accurate. This moves credential verification from a months-long manual process to a matter of minutes.
