T7 deploys coder-in-the-loop AI that reads the chart, suggests codes with evidence, flags denial risk and learns from every coder correction.
Coders are asked to read longer charts, apply more rules, and hit tighter turnaround — while payer denial logic keeps changing.
Purely automated coding is a compliance risk; purely manual coding is a throughput and cost problem.
Chart-to-code AI that surfaces ICD-10, CPT and HCC candidates with the exact chart evidence pinned next to each suggestion.
A denial-risk model scores each claim against payer-specific patterns and flags edits before submission.
Every coder edit becomes training signal — the system improves per specialty, per payer, per client.
Discharge summary, progress notes and orders are parsed and de-identified for the model context.
Candidate codes appear with pinned chart snippets; coder accepts, edits or rejects with one keystroke.
Denial-risk score and payer rules run pre-submission; high-risk claims route to a senior coder queue.
Extract structured data from invoices, forms, IDs and reports with 95%+ accuracy.
Explore serviceProduction-grade GPT, Claude, Gemini and open-source LLMs — grounded in your data.
Explore serviceProduction ML for forecasting, churn, risk and pricing — trained on your data.
Explore serviceYes — deployed in HIPAA-eligible cloud regions with BAA, PHI de-identification, audit logging and role-based access controls.
No — the pattern is coder-in-the-loop. Coders do more high-value work per hour and the org handles higher volume without linear headcount growth.
Industries where this workflow ships, the insights behind it, and the tradeoffs to weigh.
Underwriting, claims and fraud AI
AI-powered healthcare software
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