AI Chart Intelligence Platform

RiskQ360

See the whole chart. Capture every payable HCC.

Evidence-grounded · PDF-native · human-in-the-loop

An AI-powered chart intelligence platform for healthcare risk adjustment and quality. RiskQ360 reads medical records straight from PDFs — no EHR integration required — and turns them into coded, cited, audit-defensible intelligence: every HCC, every RAF dollar, every care gap, each one backed by page-level evidence and a MEAT breakdown. A coder confirms; the system never guesses unsupported.

What it does

From a 40-page PDF to a payable, defensible code set

A dual ML + LLM pipeline reads the chart, finds every legitimate code at the right specificity, proves it with evidence, and quantifies its risk and quality value — with a coder always in control of what ships.

📄

Reads the chart

PDF intake with per-page quality scoring and GPT-4o Vision OCR fallback. Network folders, Azure Blob, S3, SharePoint, scanned docs, EHR exports — no integration required.

🧬

Finds every code

BioClinicalBERT predicts HCC categories from the full text; TF-IDF retrieves precise ICD-10s across 7,903 codes; GPT-4o verifies each one — catching codes humans and single-model tools miss.

Proves it

MEAT validation, six-status negation, and page-level evidence spans behind every code. Audit-defensible by construction, RADV-ready on export.

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Quantifies the value

RAF scoring with V28 hierarchy, captured-vs-potential per member, and HEDIS care-gap closure across 100+ measures — risk and quality on one surface.

How it works

Two engines, one verified code

RiskQ360 doesn't ask a single model to guess codes. It runs a deterministic pipeline that pairs an ML predictor with semantic retrieval, then gates and verifies every candidate before a code is ever called payable.

ML

BioClinicalBERT

A multi-label HCC predictor reads the full chart and catches risk patterns the LLM misses in long documents — working on raw text, not just extracted lines.

Retrieval

TF-IDF over ICD-10

For each predicted HCC, TF-IDF searches the 7,903-code ICD-10-CM catalog — constrained to the V28-mapped subset — to find the right specificity (E11.65, not E11.9).

Verify

NegEx + GPT-4o MEAT

ConText/NegEx gating removes negated, historical, and family-history mentions; GPT-4o then verifies clinical support and Monitor / Evaluate / Assess / Treat evidence.

Every payable code carries a page-level citation and a MEAT breakdown — ICD, HCC, RAF weight, and the exact chart text that supports it. Nothing payable left behind; nothing coded without proof.
Inputs & integrations

Reads the charts you already have, where they already live

RiskQ360 is PDF-native and deploys inside your environment — on-prem, cloud, or folder-based — with CMS-HCC V28 as the reference anchor.

+ HEDIS measure specs, ICD-10-CM catalog, RADV export packages & more
RQ
Built for

Medicare Advantage, Medicaid MCOs & ACA plans

Risk adjustment and quality on capitated and value-based contracts — for coders, compliance and audit teams, quality analysts, and revenue leaders. Configurable per plan, per line of business, per measurement year.

Ready to see the whole chart?

Read the white paper for the full architecture, or walk through the deck.