Health plans lose risk-adjustment revenue and miss quality measures not because the evidence isn't in the chart — but because no one can read every chart, prove every code, and defend it on audit.
Risk adjustment and quality both come down to the same problem: finding clinically supported conditions and care events buried in long, messy clinical documents, then proving them with evidence a coder and an auditor will both accept. Today that work is manual, slow, and inconsistent — and the tools that automate it typically demand deep EHR integration and still produce codes a human can't trace. RiskQ360 closes that gap. It reads charts straight from PDFs, runs a dual ML + LLM pipeline to surface every legitimate HCC at the right ICD-10 specificity, gates each candidate through deterministic negation logic and MEAT verification, and presents the result as a payable pack with page-level citations. Risk adjustment, HEDIS quality, clinical suspicion signals, and revenue intelligence run on one platform, configurable per plan and line of business.
A single chart can run forty pages of progress notes, scanned faxes, labs, and discharge summaries. The conditions that drive RAF and the evidence that closes HEDIS gaps are in there — but they are scattered, inconsistently worded, and often negated, historical, or family-history mentions that must not be coded. Human coders are accurate but slow and expensive; they cannot review every chart, every year. Single-model AI tools are fast but hallucinate codes, miss specificity, and produce nothing a coder can audit line-by-line. And most platforms require an EHR integration project before they can see a single chart.
The cost shows up as un-captured RAF, open care gaps, and audit exposure when codes can't be defended. The opposite failure — over-coding unsupported conditions — is just as dangerous under RADV. What's missing is a system that reads everything, codes precisely, proves every code, and keeps a human in control.
RiskQ360 is an AI chart intelligence platform that ingests medical records from PDFs (with per-page quality scoring and GPT-4o Vision OCR fallback), runs five LLM extraction pipelines plus a dual ML + LLM coding engine, and produces a Payable HCC Pack, a HEDIS Quality Pack, and an Audit Pack for every chart. The ML route (BioClinicalBERT) predicts HCC categories from raw text; TF-IDF retrieval finds the precise ICD-10s; ConText/NegEx gating and GPT-4o MEAT verification confirm support. Every payable code carries an ICD, an HCC, a RAF weight, a MEAT breakdown, and a page-level evidence span. Coders Accept / Reject / Modify; everything is timestamped and audit-logged.
A single LLM asked to "code this chart" will hallucinate codes and miss specificity in long documents. RiskQ360 instead runs a deterministic pipeline in which two independent engines find candidates and a verification layer decides what is payable — nothing is coded on a single model's say-so.
A multi-label HCC predictor reads the full chart text and surfaces candidate HCC categories — catching risk patterns the LLM misses across 40 pages, working on raw text rather than pre-extracted lines.
For each predicted HCC, TF-IDF searches the 7,903-code ICD-10-CM catalog, constrained to the V28-mapped subset, matching clinical language to code descriptions to find the right specificity (E11.65 vs E11.9).
ConText/NegEx deterministically gates out negated, historical, and family-history mentions; GPT-4o then verifies clinical support and MEAT evidence and assigns a confidence score.
The dual approach is the edge. The LLM extraction route is good at obvious diagnoses but can invent codes; the ML + retrieval route catches patterns the LLM misses and lands the correct ICD-10 specificity that LLMs routinely get wrong. RiskQ360 merges both candidate sets, de-duplicates, then runs every survivor through negation gating and MEAT verification. The result captures more legitimate conditions, at higher code specificity, with fewer false positives.
Evidence-grounding & the six-status negation model. Every code links back to the exact page and character span that supports it. Mentions are classified into six polarities — active, negated, resolved, historical, family_history, uncertain — and only active conditions are payable (historical only when recaptured with current-year management evidence). Speculation is never presented as fact.
RiskQ360 decomposes chart coding into eight stages, run with parallel pipelines, so each stage contributes grounded evidence the next can build on.
Per-page text extraction with quality scoring; GPT-4o Vision OCR fallback for low-quality or scanned pages.
Demographics, clinical sentences (22 categories), risk diagnoses, HEDIS evidence, and encounters — in parallel.
BioClinicalBERT predicts HCC categories from the full chart text, independent of the LLM extraction.
TF-IDF retrieves precise ICD-10s for each predicted HCC, constrained to the V28-mapped subset.
Deterministic ConText/NegEx rules assign one of six polarities and filter non-payable mentions.
GPT-4o verifies clinical support and extracts Monitor / Evaluate / Assess / Treat evidence per code.
V28 ICD→HCC mapping with hierarchy suppression and constraining rules; RAF computed per encounter and member-year.
Payable HCC Pack, HEDIS Quality Pack, and Audit Pack — surfaced for one-click Accept / Reject / Modify.
RiskQ360 is a working surface for coders, auditors, analysts, and leaders — not a chat window. The core interaction is Accept / Reject / Add / Delete / Modify, every action timestamped and cited:
RiskQ360 is PDF-native and meets charts where they already live — no EHR integration project to start. CMS-HCC V28 is the reference anchor; LLM providers are pluggable.
RiskQ360 pre-processes every chart and presents a Payable HCC Pack: each HCC with its supported ICD-10s, RAF weight, hierarchy explanation, MEAT breakdown, and page-level evidence. A certified coder reviews and Accepts / Rejects / Modifies — turning a blank-page coding task into a confirm-and-correct workflow at far higher throughput, with the audit trail built in.
Every code is traceable to the page and span that supports it, with a MEAT checklist and confidence scores. Compliance teams export a complete audit package per member, validate vendor-submitted codes, and respond to RADV requests with documentation already assembled.
RiskQ360 extracts BP readings, A1C values, screenings, immunizations, and medications, then evaluates eligibility, numerator compliance, and open gaps for measures such as COL, CCS, BCS, CBP, and TRC — configurable per plan and measurement year.
The clinical intelligence engine cross-references labs, vitals, medications, and diagnoses to flag undercoded conditions — uncontrolled diabetes, CKD-stage discrepancies, drug-condition mismatches. Population dashboards rank opportunities by RAF-dollar impact and track annual recapture so nothing payable is left behind.
RiskQ360 runs as a containerized platform that deploys inside the customer environment — on-prem, private cloud, or folder-based — so PHI never has to leave.
React 19 + Mantine 8 + TypeScript on Vite — a knowledge-worker cockpit with HCC, HEDIS, audit, and PDF-viewer surfaces and six visual themes.
FastAPI on Python 3.12 with SQLAlchemy 2.0 and Pydantic v2, orchestrating ingestion, extraction, ML, and decisioning with feature-flag control per run.
BioClinicalBERT multi-label HCC predictor, TF-IDF vectorizer over the ICD-10-CM catalog, and a deterministic ConText/NegEx negation layer.
PostgreSQL 16 with an assertion-centric, 35+ table schema, seeded with CMS-HCC V28 mappings, coefficients, hierarchy rules, and HEDIS specs.
Configurable without code. LLM provider and model, prompt templates, chunk sizes, confidence and similarity thresholds, measurement year, and which capabilities run (risk-only, HEDIS-only, ML on/off, OCR on/off) are all controlled by feature flags — globally or per chart run.
Established risk-adjustment vendors correlate claims and require EHR integration. RiskQ360's differentiators are PDF-native deployment, a dual ML + LLM pipeline, and a clinical suspicion-signal engine — delivered inside the customer's environment.
| Capability | RiskQ360 | Typical incumbents |
|---|---|---|
| PDF-native, no EHR integration | ✓ Yes | Integration project |
| Dual ML + LLM coding pipeline | ✓ BioClinicalBERT + GPT-4o | Rules / single model |
| Clinical suspicion-signal engine | ✓ Yes | Limited / none |
| Page-level evidence & MEAT per code | ✓ Yes | Partial |
| Runs inside customer environment | ✓ On-prem / cloud / folder | Vendor cloud |
| Deployment speed | ✓ Fast — no EHR setup | Months |
Positioned against Cotiviti, Optum, Inovalon, Reveleer, and Navina, RiskQ360 trades vendor-cloud breadth for evidence transparency, deployment speed, and a coding pipeline a coder can audit line by line.
RiskQ360 is built to be measured. Every capability maps to an operational metric, evaluated at note level and member-year level with RAF-weighted precision and recall.
| Metric | What it measures | Target direction |
|---|---|---|
| Review throughput | Charts a coder confirms per hour vs. manual coding | ▲ Higher |
| HCC recall | Share of supported HCCs the pipeline surfaces | ▲ Higher |
| Coding precision | Share of suggested codes that are clinically supported | ▲ Higher |
| RAF capture | Captured vs. potential RAF per member-year | ▲ Higher |
| ICD specificity | Correct sub-code selection vs. unspecified default | ▲ Higher |
| MEAT completeness | Payable codes with documented MEAT evidence | ▲ Higher |
| Coder acceptance rate | Share of suggestions accepted without override | ▲ Higher |
RiskQ360 is designed for regulated, PHI-bearing environments. Human control and full auditability are architectural, not optional.
The AI pre-codes; a certified coder Accepts / Rejects / Modifies. No code is submitted without a human decision on record.
No ungrounded codes: each ICD, HCC, and HEDIS evidence item links to the exact page and span that supports it.
Every action — who, what, when, before/after — is recorded for RADV response and internal review.
Deploys inside the customer environment so PHI never leaves; sensitive fields are access-controlled and governed.
Deterministic clinical logic ensures only active, MEAT-supported conditions are coded payable.
Feature flags, confidence thresholds, and prompt templates are controlled per plan and audit-logged on change.
Next-Era builds evidence-grounded intelligence for the teams that run critical operations. RiskQ360 is our chart intelligence platform: it reads every chart, codes at the right specificity with a dual ML + LLM pipeline, proves every code with MEAT and page-level evidence, and keeps a human in control — so health plans capture what they're owed, close the gaps that matter, and defend every code on audit.
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