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32-hour TAT median · 89% auto-approve · 78% appeal overturn

Prior auth, 32 hours. Not eight days.

Prior authorization is where care delays and revenue delays come from the same root cause. The packet that takes 8 days to clear is the packet a clinician started, a staff member partially completed, and a coordinator finished in the wrong portal three days later. We replaced that workflow. The AI assembles packets from EHR data, routes by payer rule, polls status every 4 hours, and flags only the ones needing human judgment.

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89% initial auto-approval Payer-rule library, quarterly refresh 4-hourly status polling
The packet outcomes

Where every 100 packets end up.

The hardest part of prior auth automation is being honest about what AI cannot do. The funnel to the right shows where 100 packets we submit actually land. 89 are approved on initial submission. 6 trigger payer documentation requests we resolve within 24 hours. 3 require clinical revision that only the ordering clinician can make. 2 are clean denials we may or may not appeal.

The 89 percent first-pass number is real, measured, and based on our active book. The compression is not magic; it is the elimination of human data-entry delays and the deterministic application of payer-specific rules at the moment of submission.

Per 100 submitted packets · active book · last 90 days
89
Approved on initial submissionNo rework, no appeal
6
Payer documentation requestResolved within 24 hours · 78% approve after
3
Clinical revision requiredRouted to ordering clinician for additional clinical detail
2
Clean denial · appeal decision pendingPredicted overturn 78% · dollar-value triage
Median time to first determination · 32 hours · same-day approvals 38%
The capabilities

Six capabilities. Compression every step.

Each capability is real production code. Every packet runs through the same workflow whether it is an ABA 97153 reauthorization, a high-cost imaging study, or a specialty drug J-code. The AI does not care about complexity; the payer rule library carries the differences and applies them deterministically.

01 · Packet assembly

EHR pull, structured.

Demographics, clinical documentation, prior visit notes, functional assessments, and payer-specific auth form fields pulled automatically from the EHR. Coordinators see only what needs human judgment, not the data entry. Eliminates the half-day delay between order and submission.

02 · Payer routing

Right portal, every time.

Payer-rule library carries the active submission channel per service code per payer (portal, fax, EDI). Wrong-portal misfile delays add 2 to 4 days on average; our deterministic routing eliminates them. Library refreshed quarterly when payer policies change.

03 · Status polling

276/277 every 4 hours.

4-hourly business-day polling on submitted packets. Determinations get caught the same business day instead of next-cycle. Same-day approvals now account for 38 percent of total volume across the active book.

04 · Appeal triage

Predict overturn probability.

When a packet denies, the AI predicts overturn probability from payer, service code, denial reason, and historical appeal outcomes. AI-flagged appeals have a 78 percent overturn rate vs 41 percent industry average. Dollar-value-prioritized appeal queue.

05 · Clinician nudges

WhatsApp · SMS specific questions.

When clinical revision is needed, the AI sends the ordering clinician a specific question via WhatsApp or SMS (not a generic 'please complete the form'). Clinicians report 3 to 5 hours per week on auth-related work vs the 13 hours per week the AMA 2025 physician survey documents.

06 · Reauth automation

Balance trigger, auto-resubmit.

When authorization balance approaches exhaustion (80 and 95 percent of authorized units), the system queues the reauthorization packet, pulls the latest assessment, and submits before expiry. Zero unbilled auth-expired ABA sessions over 12 months on the active book.

How the work runs

Four steps. Sub-day median.

From the moment an order is placed to the moment the determination posts, here is the work CredPro and our coordinators execute.

Step 01

Assemble the packet.

EHR pull builds demographics, clinical notes, functional assessments, and payer-specific form fields. AI gap-checks against the payer rule library and flags any missing data points before submission.

Step 02

Submit via right channel.

Payer rule library routes to the correct portal, fax queue, or EDI endpoint. Submission receipt logged. Cleared packets enter the polling queue immediately.

Step 03

Poll for determination.

276/277 status query every 4 hours during business days. Determination posts as soon as the payer system updates. Approved auths flow into the authorization tracker and unlock claim submission.

Step 04

Triage the exceptions.

Documentation requests assemble automatically and respond within 24 hours. Clinical revision routes to clinician with the specific question. Denials run through overturn prediction and dollar-value triage for the appeal queue.

What clients see

Measured outcomes from auth AI.

Across our active book. Anonymized; individual results depend on payer mix and order volume.

32hrs
Median time to determination
Industry average is 8 days. Compression comes from packet auto-assembly, payer-specific routing eliminating misfile delays, and 4-hourly status polling catching determinations the same business day.
89%
Initial auto-approval rate
Approved on first submission without rework or appeal. The remaining 11 percent split between payer doc requests (6), clinical revision (3), and clean denials (2). Honest measurement on our active book.
78%
Appeal overturn rate
On AI-flagged appeals where the model predicts a high overturn probability. Industry average is 41 percent. The lift comes from dollar-value triage and clinical documentation preparation aligned to the payer's specific overturn-history pattern.
Common questions

Frequently asked questions: AI for prior auth.

What does the AI actually automate in prior authorization?
Three things. First, packet assembly: pulling demographics, clinical documentation, prior visit notes, and the payer-specific auth form fields automatically from the EHR. Second, submission: routing to the right payer portal or fax queue based on the active payer rule for the service code. Third, status polling: 276/277 queries every 4 hours during business days until the determination posts. Coordinators see only what needs human judgment, not the data entry.
What is the TAT compression number?
Across our active book, prior auth turnaround compresses from an industry average of 8 days to a median of 32 hours. Sources of the compression: packet auto-assembly eliminates the half-day delay between order and submission, payer-specific routing avoids the wrong-portal misfile delay (which adds 2 to 4 days on average), and 4-hourly status polling catches determinations the same business day instead of next-cycle. Same-day approvals are now around 38 percent of total volume.
What is the auto-approval rate?
89 percent of packets we submit get approved on initial submission without rework or appeal. The 11 percent that do not split as follows: 6 percent are payer-side documentation requests that we respond to within 24 hours with a 78 percent overturn rate, 3 percent require clinical revision (the order needs supporting documentation we cannot generate, so it routes to the ordering clinician), and 2 percent are clean denials that may or may not be worth appealing depending on dollar value.
How does AI handle the payer-specific rules?
We carry a rule library per payer per service code, refreshed quarterly when payer policies change. The rule library covers required documentation, valid auth forms, valid submission channels (portal, fax, EDI), required clinical data points (functional assessment, prior conservative treatment, imaging), and time-validity windows. The library is the same one our certified specialists work from; AI just applies it deterministically at the moment of submission instead of relying on human memory.
What happens when an auth gets denied?
Three paths. First, the AI predicts whether the denial is overturnable based on the payer, the service code, the denial reason code, and the appeal history. Second, if overturnable, the AI assembles the appeal packet with the supporting documentation the model thinks will succeed. Third, the packet routes to a senior specialist who reviews and submits. Appeal overturn rate on our active book is 78 percent for AI-flagged appeals, compared to a 41 percent industry average.
How does this connect to authorization tracking and ABA?
Tight loop. Authorization tracking surfaces the moment a balance is approaching exhaustion (typically at 80 and 95 percent of authorized units). The system automatically queues the re-authorization packet, pulls the latest assessment from the EHR, and submits to the payer before the active auth expires. The result on our active book is zero unbilled auth-expired ABA sessions over the last 12 months.
What about specialty drugs and high-cost imaging?
Yes. Specialty drug auths (J-codes, infusions) and high-cost imaging (CT, MRI, PET) follow the same workflow with additional clinical-data requirements baked into the payer rule library. Concurrent review handoff for inpatient stays is also supported. The dollar value of these categories per packet is much higher than ABA or routine specialist visits, so TAT compression directly compounds revenue.
Is there clinician burnout reduction we can measure?
Yes. Physicians and their staff spend an average of 13 hours per week on prior authorization work, per the AMA 2025 prior authorization physician survey. Our active-book clinicians report 3 to 5 hours per week post-implementation. The reduction comes from packet auto-assembly removing the data entry, AI surfacing only the cases needing clinical judgment, and WhatsApp/SMS nudges asking the clinician one specific question rather than a generic 'please complete the form' request.

Send 30 days of auth packets. We send back the TAT map.

A free 30-day prior auth audit. Drop your last 30 days of submitted auths, denials, and turnaround data. We return a four-page audit covering median TAT by payer and service code, predicted automation rate, denial overturn opportunity with dollar values, and a 90-day fix plan. A senior partner on the call.

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