The Short Answer
Prior-authorization automation is sold on payer breadth: Myndshft, now part of DrFirst, states over 600 payers in its rules engine. That covers the payer-facing half. The AMA's 2025 survey still puts prior authorization at 13 hours of physician and staff time a week, because the clinical packet is assembled by hand. With ibl.ai you own all the code and the data, so agents read the chart in place.
A faster submission queue and a shorter wait for the patient are two different measurements, and most prior-auth automation only improves the first one.
What does "prior authorization across 600+ payer plans" actually mean, and whose claim is it?
It is a vendor coverage claim about payer connectivity, not an industry fact, and it is worth attributing precisely.
The number traces to Myndshft, acquired by DrFirst, whose site states over 600 payers maintained in its rules engine library and real-time eligibility and benefit verification covering 94% of covered U.S. lives.
What that breadth buys is real and specific: knowing which plan requires authorization for which code, where the request goes, and what the response status is.
It is a directory and a routing problem, solved well.
On HIPAA, be careful about what any such claim asserts. HIPAA has no certifying body; a vendor stating it deploys on HIPAA-compliant infrastructure is describing its own posture, and the covered entity remains accountable under its business associate agreement regardless.
So "AI agents process prior auth across 600+ payer plans under HIPAA" compresses a company's coverage claim and a legal obligation into something that sounds like a settled industry capability. It is neither.
Where do the minutes in prior authorization actually sit for a clinician?
In the chart, before the request is ever submitted — and the published figures measure several different quantities, which is where the usually-quoted numbers go wrong.
The 2025 AMA Prior Authorization Physician Survey, fielded among 1,000 practicing physicians in December 2025 and released 13 May 2026, reports 13 hours of physician and staff time each week, 40 prior authorizations per physician per week, 32% saying requests are often or always denied, and 95% saying prior authorization delays access to necessary care.
On documentation time, a widely-circulated "40 minutes per patient" figure is not traceable to a published documentation-time study.
The closest published figures measure total EHR time per visit: 36.2 minutes per primary-care visit (Rotenstein et al., JAMA Network Open, 2024), and 40.9 minutes in an earlier analysis.
Two published measurements, of different things:
A descriptive study in Annals of Internal Medicine found physicians spent 16 minutes 14 seconds of EHR time per outpatient encounter — 33% chart review, 24% documentation, 17% ordering — across roughly 100 million encounters, 155,000 physicians and 417 health systems (reported January 2020; the study).
The broader time-and-motion result is Sinsky et al., Annals of Internal Medicine, 6 September 2016: during the office day physicians spent 27.0% of total time in direct clinical face time and 49.2% on EHR and desk work, with 21 of 57 observed physicians reporting one to two further hours nightly.
These measure different quantities: EHR-log time per encounter in the first case, and an observed share of the office day in the second.
Chart review is the largest single slice of EHR time, and chart review is exactly what a medical-necessity packet demands.
Why does automating the payer-facing half not shorten time-to-patient?
Because the payer-facing half is the part that is already being compressed by regulation, and the clinical half is the part that is not.
Under CMS-0057-F, impacted payers must send prior-authorization decisions within 72 hours for expedited requests and seven calendar days for standard requests, must give a specific reason for denials beginning 1 January 2026, and must operate a FHIR Prior Authorization API beginning 1 January 2027.
Prior authorization for drugs is excluded from these provisions.
A payer response that is already capped at 72 hours cannot be the dominant term in a two-week delay.
The dominant term is upstream: the interval between the clinical decision and a complete, defensible packet leaving the practice.
That interval is made of chart review, hunting for the prior therapy that establishes step-through, locating the imaging report, and re-stating criteria the payer published last month.
Automating submission when the packet is the constraint produces a shorter queue with the same wait. Throughput improves, time-to-patient does not.
Measure the second one. Time-to-patient is the clock from clinical decision to the patient receiving the service, and it is the only number the patient experiences.
What makes the clinical-documentation half of prior authorization a data-layer problem, not a model problem?
Because assembling a medical-necessity packet is a retrieval and provenance task, and a better model does not improve either.
Current models draft a competent letter from supplied facts; that part has been solved for a while.
The work is supplying the facts: notes, labs, imaging reports, medication history and prior-therapy evidence, which in most health systems sit across an EHR instance, an imaging archive, a lab network and at least one outside institution.
The instinct is to extract it all into one repository the agent can query.
In healthcare that creates a second problem — every copy of PHI is a new compliance surface to secure, audit, retain and destroy, and a medication list extracted last night does not reflect this morning's change.
The alternative is reading in place: role-scoped access to each system of record, with the packet assembled at query time and every retrieved fact carrying its source system and as-of date.
Provenance is not optional here. A packet that cites a lab value the payer can trace survives an appeal; one that cannot is a denial waiting to be re-worked. This is the same constraint that decides whether clinical AI works at all, applied to a revenue-cycle workflow.
The cost shape follows from the same place, and it is worth checking against what prior-authorization AI actually costs: per-transaction and per-clinician pricing scale with headcount and volume rather than with the work performed, which is the wrong shape for a workload that runs 40 times per physician per week.
How does ibl.ai handle the clinical half of prior authorization?
By running inside the health system's perimeter, where the chart already is.
With ibl.ai you own all the code and the data.
The platform runs on the institution's own infrastructure with full source code under a perpetual license, so PHI stays inside the covered entity's boundary unless the institution decides otherwise.
It is model-agnostic across any LLM, so a model can be swapped without rebuilding the workflow. It is usage-based with no per-seat pricing, so cost tracks authorizations processed rather than clinicians employed.
And it can deploy anywhere: your own cloud, on-premise, GovCloud, or a fully air-gapped network.
Agents read systems of record in place under role-scoped permissions enforced server-side, every retrieval is audited, and access control binds to the existing identity provider.
Integration is via HL7 FHIR and standard healthcare APIs into Epic, Cerner/Oracle Health, athenahealth and Meditech, and into lab and imaging systems, so the packet is assembled from the systems of record themselves.
1.6M+ users across 400+ organizations run the platform this way, including NVIDIA, MIT, and Syracuse University.
For a compliance officer the operative property is inspectability: the code that touches PHI can be read, not merely attested to.
ibl.ai is family-owned and operated from New York, NY.
Related reading: what AI prior authorization actually costs in 2026 — the per-letter token math behind the same workflow; and healthcare AI's bottleneck was never the model — the read-in-place architecture this depends on.
Sources: payer-coverage and eligibility claims from Myndshft; burden figures from the 2025 AMA Prior Authorization Physician Survey, released 13 May 2026; EHR time per encounter from the Annals of Internal Medicine descriptive study as reported in January 2020; total EHR time per visit from the AMA's report on Rotenstein et al., JAMA Network Open 2024 and an earlier analysis; the 27.0%/49.2% time-and-motion split from Sinsky et al., 6 September 2016; decision timeframes and API dates from the CMS-0057-F fact sheet.