Models are excellent at reading messy clinical evidence, and they stay in the stack. LogicPearl makes the final readiness call from exact policy rules, the same way every time, with the source for each criterion.
On identical inputs, 8 model groups split 20 approve to 4 deny, and their reasons drifted between runs.
| An AI checklist | A LogicPearl result | |
|---|---|---|
| Source excerpt per criterion | Often omitted or summarized | Every criterion tied to source PDF text |
| Clause coverage accounting | Usually unmeasured | 99.9% measured on BCBSMA; misses preserved as review work |
| Nested AND/OR & exceptions | Can flatten or invert | Preserved in the decision logic |
| Table semantics | Frequently flattened | ~118k rows interpreted into checkable rules |
| Terminology provenance | Implicit / hidden | Explicit mapping status per criterion |
| Repeatable replay | Can vary on the same packet | Same packet and policy version → same result |
| Reviewer state | Answer only | Promotion gated, sign-off tracked |
| Packet-to-policy evidence link | Not guaranteed | Click-through packet and policy excerpts |
Analysts translating policy PDFs into configuration guess at coverage and take weeks per policy. The Blue Cross Blue Shield of Massachusetts corpus now arrives with coverage measured: 99.9% of clauses, every miss listed, and a semantic diff for every revision. The same accounting can run for any payer policy set.
The status quo is reviewers interpreting policy from memory and audits reconstructed after the fact. The cost is hard to see until an overturn pattern, an audit, or a compliance finding makes it visible all at once.
Comparing vendors? Here are the five questions to ask before you trust software with your denials, with a printable scorecard. Already have a prior-auth tool? See how LogicPearl fits alongside prior-auth automation.
Walk through one prior-auth case on published policy, then run a shadow-mode pilot on yours.