Healthcare AI automates prior authorization backwards: Oprox CEO
NEW JERSEY, UNITED STATES — Prior authorization (PA) consumes $35 billion annually across American healthcare — and the artificial intelligence (AI) tools deployed to fix it are largely automating the wrong part of the problem.
Writing in a Forbes Tech Council analysis, Venkata Ramya Ganti, Founder and CEO of Oprox, charges that most AI prior authorization solutions accelerate form submission without addressing the reasoning and recovery failures that drive prior authorization denials.
Prior auth AI stops at submission
Physicians and clinical staff spend roughly 13 hours weekly on prior authorization requirements. Yet 88% of denials go unchallenged — not because cases lack merit, but because 62% of physicians believe appeals will not succeed and 48% cite insufficient staff time to pursue them.
“The industry has been solving the wrong problem,” Ganti wrote. “Prior authorization is not fundamentally a form-filling challenge. It is a reasoning challenge at the front end and a recovery challenge at the back end.”
When 80.7% of appealed prior authorization denials are ultimately overturned — but only 11.5% of denied requests in Medicare Advantage are ever appealed, per CMS data — the gap is not a documentation problem.
It is a reasoning and resource problem that form-speed automation cannot address.
The result is a system where 78% of physicians report that prior authorization delays cause patients to abandon treatment entirely — a patient safety outcome masked by workflow metrics that measure submission speed, not approval rates or appeal success.
Real automation must reason before submission
Ganti identifies two distinct failure layers. Pre-submission AI tools accelerate data movement without replicating the clinical reasoning that determines payer approval likelihood. Post-denial tools are largely absent: most solutions stop at submission, leaving denial pattern analysis and outcome learning unaddressed.
Ganti frames the solution direction plainly: “The future of prior authorization will be defined by how intelligently systems reason before submission and how effectively they learn after denial.”
Next-generation AI prior authorization systems must simultaneously understand clinical context, payer-specific documentation requirements, and payer reviewer logic at the point of submission — and treat every denial as a training signal rather than a write-off.
For healthcare outsourcing providers managing prior authorization support, coding, and appeals workflows, Ganti’s framework identifies where the operational gap sits.
Offshore teams handling prior authorization are well-positioned to close the recovery layer — triaging denials, analyzing payer patterns, and managing appeals that automated systems leave on the table.
The $35 billion is not being spent on a hard problem. It is being spent on the wrong solution to a fundamentally solvable one.
