Should a small practice pilot an AI prior authorization tool? How to decide

What to evaluate in any prior authorization tool

• •Source-grounded, not guessing. The tool should cite the payer policy or clinical evidence behind every recommendation, so your staff can verify it. If it cannot show its source, it can hallucinate, and a wrong criterion is worse than none.
• •Provider-side and documentation-first. It should check your clinical note against the payer’s own medical-necessity criteria before you submit, and help build policy-cited appeals when a denial hits. That is different from a clearinghouse or a payer-side tool.
• •No multi-month EHR project. You should be able to start with a copy-and-paste or de-identified workflow in days, not wait on an Epic committee. Deep integrations can come later.
• •Human in the loop. Every output should be an AI-generated draft your team reviews and submits. Nothing should be filed under your providers’ names automatically.
• •Honest data handling. Ask exactly where the product is on compliance. A credible vendor tells you plainly rather than overstating it, and a pilot can run on de-identified or sample data so no protected health information is at risk.
• •Measurable on your cases. The only proof that matters is your own results: time saved, gaps caught, and how your denials trend, measured against your baseline, not a vendor average.

Why a low-risk pilot beats a big purchase

No vendor can guarantee a payer’s decision, so do not buy on a promised approval rate. Buy on measured results. A pilot lets you capture your current baseline, run real (de-identified) cases through the tool, and compare, before any long-term commitment. The downside is capped at the time you spend; the upside is a documented before-and-after on your own queue.

What a good pilot looks like

• •A real free period. Enough time to run actual cases, not a 7-day trial that ends before you see a pattern.
• •Your real, de-identified cases. Bring a denied or tricky prior authorization with identifiers removed. A demo on a vendor’s sample case proves nothing about your payers.
• •A results report before you commit. A good partner shows you the time saved and gaps caught and lets you opt out if the numbers are not there.
• •No IT project to start. If onboarding requires an integration before you can test anything, the pilot is not really low-risk.
• •Clear data boundaries. De-identified or sample data during the pilot, with a Business Associate Agreement and full controls in place before any identified records are handled.

Why now

Two forces make this the moment to test provider-side AI. Payers have automated their side of prior authorization and can generate denials in seconds, while most practices still respond with portals, faxes, and phone calls. And under the CMS Interoperability and Prior Authorization final rule, affected payers must support electronic prior-authorization workflows with new decision-timeframe and transparency requirements, with key provisions effective January 1, 2027. Practices that get fluent with electronic, policy-driven prior authorization now will be ready for that shift.

How Praxigen fits

Praxigen is a provider-side, source-grounded prior authorization workspace built for this exact evaluation: it checks documentation against payer criteria before submission and drafts policy-cited appeals, every output an AI-generated draft for human review. The founding pilot is designed to be low-risk: the first 2 months are free, then a flat $299 per month for the whole practice if you opt in after a month-2 results report. It runs on de-identified or sample data, with no EHR integration required to start.

How to start a low-risk prior authorization pilot

1. 1Book a case review. Bring one denied or tricky prior authorization with patient identifiers removed.
2. 2See it on your own case. Review the payer’s medical-necessity criteria and the cited evidence behind the recommendation, live, on your real (de-identified) case.
3. 3Run the free period. Use the workspace on your own cases for the first 2 months at no cost, on de-identified or sample data, with weekly check-ins.
4. 4Decide on the numbers. At month 2, review a results report on time saved and gaps caught, then opt in at the flat founder rate or opt out and owe nothing.