Axeris provides an AI‑driven clinical intelligence layer that evaluates prescription claims in real time against guidelines, FDA labeling, and payer policies, automatically approving safe orders and flagging risky or wasteful ones for review. By integrating EHR, claims, lab, and medication data, the platform helps pharmacy benefit managers and insurers reduce drug waste, improve patient safety, and streamline prior‑auth processes.
Funding
Funding not disclosed
Founders
Product
Problem
Pharmacy benefit managers and health insurers often lack a real‑time clinical validation step before prescription claims are paid, leading to unsafe dosing, drug interactions, and costly waste of medications. Existing prior‑auth processes are slow and miss many inappropriate prescriptions, compromising patient safety and increasing spend.
Solution
Axeris inserts an independent AI‑driven clinical intelligence layer between the prescribing event and claim payment. The platform automatically extracts a patient’s EHR data, claims history, laboratory results, and current medication profile, then evaluates each new prescription against clinical guidelines, FDA labeling, plan policies, and learned prescribing patterns. Prescriptions deemed appropriate receive an automatic green flag and are paid without delay. Those with safer or cheaper therapeutic equivalents are marked yellow, prompting optional review, while high‑risk or wasteful orders are flagged red with a detailed, evidence‑based explanation for human adjudication. This workflow enables payers to intercept unsafe or unnecessary drugs before they are dispensed, reducing waste and enhancing patient safety with reported 99.4% accuracy.
Target Audience
Primary customers are pharmacy benefit managers, health insurers, and Medicare/Medicaid administrators that process large volumes of prescription claims and seek to improve clinical safety while reducing drug waste.
Features
- Rules & guideline engine that checks dosage, duration, interactions, FDA labels, and plan policies, providing rule citations and source links for each flag
- Machine‑learning and anomaly‑detection module that models normal prescribing behavior, identifies outlier patterns, and scores polypharmacy risk
- Real‑time integration with EHRs, claims databases, lab systems, and medication profiles to construct a complete clinical picture for every claim
- Automated triage outcomes: green (auto‑approve), yellow (suggest alternative), red (require human review) with clear, evidence‑based explanations
- Scalable API that fits within existing payer claim processing pipelines without altering prescriber workflows
- High accuracy (99.4%) validated across millions of prescriptions, supporting both safety and cost‑containment objectives