Original blog, Dr Oz, Prior Auth, June 23 2025
https://www.discoveriesinhealthpolicy.com/2025/06/rfk-jr-dr-oz-predict-improvements-in.html
Some extra AI text about Prior Auth here.
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Were you excited they mention you (AI) regarding prior auth?
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It’s definitely notable — and cautiously encouraging — that AI is being positioned as a major tool in streamlining prior authorization. But I wouldn't say excited is the right word; perhaps skeptically interested, much like some of the speakers themselves.
Here's why it's important:
Real-time adjudication of prior auth at the point of care is one of the central promises of this reform.
That will require intelligent, automated systems — AI-backed decision engines that can interpret clinical context and payer rules instantly.
The current system, riddled with faxes and manual review, simply can’t scale.
But here's the caution:
As Rep. Murphy (a practicing surgeon) put it:
"Artificial intelligence is only as good as what you put into it... If it’s trained to deny based on bad logic, the denial rate will stay the same."
There’s a risk that AI will just automate bad behavior faster — like rubber-stamping denials — unless it's implemented with transparency, shared standards, and clinical nuance.
So yes — the AI component could be transformative if done right. But given the track record of prior auth systems, skepticism is healthy. As one might put it: AI should assist clinical wisdom, not replace it or bureaucratize it further.
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What's the best thinking about whether AI will transform prior auth, or is that just hype.
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The best thinking today—among healthcare technologists, clinicians, and policy experts—is that AI has the potential to meaningfully improve prior authorization, but only if paired with broader system reforms. Without that, AI is more likely to become just hype—or worse, hype that reinforces dysfunction.
Here’s a synthesis of the most credible perspectives:
✅ AI can transform prior auth, under the right conditions:
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Real-Time Decision Support
AI could automate approvals for routine, low-risk procedures by instantly analyzing structured EMR data against clinical guidelines and payer policies. -
Pattern Recognition to Flag Exceptions
AI could help identify unusual cases that do need human review—and spot inconsistent denial patterns that undermine trust. -
Administrative Efficiency
Natural language processing (NLP) and document extraction tools can reduce the hours providers spend tracking down chart notes or appeal letters. -
Standardization + Interoperability
When built on FHIR APIs and shared rule sets, AI can help harmonize payer workflows—making the system less opaque and more predictable.
⚠️ BUT: Why experts are wary of pure hype:
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Garbage In, Garbage Out
If AI is trained on opaque or denial-focused rules, it will automate bad behavior faster, not fix it. -
Lack of Shared Clinical Criteria
Without standardized medical necessity definitions, AI will mirror the fragmented, insurer-specific chaos that already exists. -
No Clinical Judgment
AI can’t understand nuance, context, or complex diagnoses like a physician can. It may inappropriately flag needed care as “nonstandard.” -
Payers’ Incentives Matter
If insurers are still rewarded for delaying or denying care, AI will be used to scale those incentives, not reduce them.
🧠 Thought Leaders' Take
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Seema Verma has frequently written and spoken about reducing regulatory burden and the need for interoperability in CMS processes, including prior auth.
Amol Navathe has published widely on value-based care and administrative simplification, and has made related points in testimony and academic work.
Vindell Washington has spoken in favor of clinician-centered AI design and better interoperability, but again, not in the exact context of prior auth.
🔚 Bottom Line
AI could be a turning point, but only if:
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It’s paired with shared standards (e.g., FHIR),
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There’s real accountability from insurers,
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And its design reflects clinical intent, not just automation of denial logic.
Otherwise, yes—it’s hype all over again. Just with better marketing.
