The AI reads your patient's chart, writes the perfect insurance request in each payer's preferred language, and submits it automatically—so your staff doesn't have to.

ClaimGlide's core ML pipeline ingests structured and unstructured clinical data from EMR systems via HL7 FHIR APIs, applies named-entity recognition (NER) and clinical NLP to extract diagnosis codes, treatment plans, and supporting documentation, then feeds this into a fine-tuned LLM that generates prior-auth request narratives optimized for each specific payer's known approval criteria and preferred verbiage. The system cross-references historical approval/denial data to select the highest-probability framing for each request, then auto-submits via RPA bots to payer portals or electronic submission channels. This eliminates hours of manual data entry, phone calls, and fax-based workflows per request, dramatically reducing administrative burden and accelerating time-to-authorization for private practices.

It's like having a multilingual translator who knows exactly how each insurance company likes to hear "please approve this," and never gets tired of filling out forms.

The AI spots which requests are likely to get denied before you even submit them, then automatically writes and files a bulletproof appeal if one does get rejected.

ClaimGlide trains classification models on historical prior-auth outcomes—approval rates, denial reasons, payer-specific patterns, diagnosis-procedure pairings, and documentation completeness—to generate a pre-submission denial risk score for every request. High-risk requests are flagged for additional documentation or language adjustments before submission, proactively reducing denials. When denials do occur, the system's LLM pipeline automatically parses the denial reason code and letter, retrieves relevant clinical evidence from the patient's EMR, cross-references payer policy documents and clinical guidelines (e.g., MCG, InterQual), and generates a structured appeal letter with cited medical necessity arguments. For payers requiring phone-based appeals, ClaimGlide deploys voice AI agents to navigate IVR systems and present appeal arguments. This closed-loop system continuously learns from appeal outcomes to improve both pre-submission optimization and post-denial recovery.

It's like having a lawyer who reads the judge's mind before trial, rewrites your argument to match, and if you still lose, instantly files a perfect appeal with all the receipts.

The AI watches every pending authorization like a hawk, predicts which ones are about to stall, and automatically nudges the right people or systems before anything falls through the cracks.

ClaimGlide deploys a multi-agent orchestration system that continuously polls payer portals, fax inboxes, and electronic response channels via RPA bots and API integrations to maintain a real-time status dashboard of all pending prior-authorizations across a practice's entire patient panel. A time-series forecasting model trained on historical payer response patterns predicts expected decision timelines and flags requests at risk of exceeding SLA thresholds or approaching treatment date deadlines. When delays are detected or predicted, the system autonomously triggers escalation workflows: re-submitting requests, initiating follow-up calls via voice AI, sending provider notification alerts, or escalating to human staff with pre-populated context summaries. The orchestration layer also updates practice management systems with confirmation numbers, approval documents, and expiration dates, ensuring downstream scheduling and billing workflows remain synchronized. This transforms prior-auth tracking from a reactive, manual process into a proactive, self-healing system.

It's like having an air traffic controller for your insurance paperwork—constantly scanning the radar, rerouting anything that's about to stall, and making sure every authorization lands safely before the patient's appointment.