Model prior authorization automation without automating clinical denials.
Built for payer operations, clinical services and digital leaders. Auto-approval, reviewer productivity, avoidable appeals and provider abrasion in one CMS-aware model.
Your inputs
Benchmarks show typical enterprise ranges — override every field with your own numbers.
All lines of business in scope
Benchmark: 500k–5M for a regional to national plan
Intake, clinical review, documentation and QA
Benchmark: $18–$40 blended across nurse and MD review
Deterministic approvals only — denials stay with clinicians
Benchmark: 35–60% on codified service categories
From chart summarisation and criteria pre-population
Benchmark: 18–30%
Share of requests that generate an appeal
Benchmark: 5–12%
Clinical review, correspondence and regulatory handling
Benchmark: $120–$300
From consistent criteria and complete first-pass documentation
Benchmark: 15–30%
Inbound calls chasing authorization status
Benchmark: 0.25–0.60
Benchmark: $5–$9 fully loaded
Interoperability APIs, clinical NLP, policy engine and governance
Benchmark: $800k–$2M
Review, appeals and provider-contact cost removed across the authorization lifecycle.
Directional estimate. Auto-approved cases retain 10% of review cost for audit sampling; adverse determinations always remain with licensed clinical reviewers.
Three-scenario view
Finance reviewers expect a range. These scenarios flex adoption and implementation cost around the model you entered.
Slower adoption, higher integration effort
Your inputs as entered
Strong sponsorship, clean data, phased scale-up
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Send us the brief and a delivery lead validates these assumptions against your data, then replies with indicative scope, timeline and commercial options.
CalculatorModel prior authorization automation without automating clinical denials. — routed to this team
How enterprise leaders use this model
- Which prior authorization steps can safely be automated?
- Intake, clinical document extraction, policy matching and auto-approval against deterministic criteria. Denials and clinically ambiguous cases stay with licensed reviewers — the model never automates an adverse determination.
- How does CMS interoperability change the business case?
- CMS-0057-F turnaround requirements make manual review economically hard to sustain. Automating intake and criteria matching is usually the fastest path to compliant turnaround without adding nurse reviewer headcount.
- Where do appeals savings come from?
- Consistent criteria application and complete documentation at first pass reduce avoidable denials, which in turn cuts appeals volume. Each avoided appeal removes both review labour and downstream call volume.
- What auto-approval rate is realistic?
- Plans typically reach 35–60% auto-approval on high-volume, well-codified service categories such as imaging and durable medical equipment.
What is the ROI of AI in payer prior authorization?
Prior authorization AI value comes from auto-approving requests that clearly meet criteria, lifting clinical reviewer productivity on the remainder, avoiding downstream appeals caused by inconsistent determinations, and removing provider status calls from the contact center — all inside CMS turnaround requirements.
Ungated — results appear instantly, no email required.
What you enter
- Annual prior authorization request volume
- Share meeting criteria cleanly (auto-approvable %)
- Loaded cost of a nurse or clinical reviewer touch
- Appeal rate and cost per appeal
- Provider status-call volume and cost per call
How it is calculated
- 1.Separate auto-approvable requests from those needing clinical review.
- 2.Value removed reviewer touches at the loaded clinical cost.
- 3.Apply the appeal-rate reduction from more consistent determinations.
- 4.Value deflected provider status calls at contact-center cost.
- 5.Subtract platform, integration and compliance investment.
What you get back
- Reviewer capacity returned
- Appeals and status calls avoided
- Turnaround-time improvement against CMS thresholds
- Net annual benefit and payback
Built for: Utilization management, clinical operations and digital leaders at health plans.
Can AI issue a prior authorization denial?
No. Auto-approval against published criteria is the automatable path; adverse determinations require qualified clinical review. Business cases that assume automated denials are both non-compliant and unbuildable.
Where does prior authorization AI create the most value?
In the clean-approval lane and in the provider communication around status, which together account for a disproportionate share of volume without carrying clinical determination risk.