The missing information loop: how AI and ML solutions are fixing prior authorization at its source - Onix
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Prior authorization is not broken because the concept is flawed. It is broken because the process depends on manual review of complex documentation at volumes that human reviewers cannot sustain accurately or quickly. A significant share of prior authorization requests are rejected not on clinical grounds but because a single lab result was missing, or a physician's notes did not explicitly reference a patient's therapy history. Each of those rejections triggers a resubmission cycle — adding days to the approval timeline, consuming clinical staff hours, and delaying patient access to treatments that were appropriate from the start. This is a process failure, and it is one that AI and ML solutions are specifically positioned to resolve. The prior authorization agent built on Google Agentspace addresses this at the source of the failure: the missing information gap. Rather than issuing a rejection when documentation is incomplete, the agent identifies what is missing and draft...
