Will AI fix prior authorization in healthcare — or make it worse?

What's happening

Prior authorization is one of healthcare's most hated processes: doctors must get insurer approval before procedures or prescriptions. Now, startups and big AI players promise to automate this workflow, reducing wait times from days to minutes. But critics warn that poorly calibrated algorithms can deny treatments faster — and with less transparency.

Technical impact

AI systems analyze electronic health records, clinical guidelines, and coverage history to issue automatic decisions. Natural language models extract information from unstructured documents, while neural networks classify requests as approved, denied, or pending. The problem: biases in training data can replicate historical inequalities, and lack of explainability makes it hard to contest denials.

Business and growth perspective

For healthcare companies, automating prior authorization means drastic cost reduction and higher patient satisfaction — two critical retention drivers. An insurer processing 10,000 requests per month can cut 80% of analysis time, freeing teams for complex cases. In the growth funnel, approval speed directly impacts plan conversion and customer loyalty. However, mass errors can trigger reputation crises and regulatory actions.

10Dobro's view

At 10Dobro, we see AI automation as a scale lever, not a black box. AI systems must be designed with human feedback loops, clear performance metrics, and data governance. It's not just replacing paper with code, but building pipelines that combine efficiency with auditability. In audiovisual, we apply similar logic: automation of editing and content analysis only works if humans retain creative and strategic control. The same applies to healthcare — AI accelerates, but final decisions demand accountability.