Meet ClaimIQ

A Unified AI Platform for Healthcare Revenue Cycle Management

ClaimIQ brings together document intelligence, contract analysis, and clinical evidence review into a single, integrated workflow—deployed across the nation’s largest health systems.

ClaimIQ: Three Integrated Platforms

DocIQ

An AI-powered appeals workbench that reads UB-04s, interprets medical records, and generates tailored, evidence-based appeal letters grounded in clinical guidelines, payer policies, and regulations—all built in and continuously updated.

ContractIQ

Payer contracts and addenda are parsed into structured, queryable rule databases. Expected reimbursement is calculated by weaving together multiple data sources—claim data, Medicare fee schedules, CMS weight tables, and more—enabling precise pricing at scale.

ClinIQ

Denial outcomes and clinical documentation signals are transformed into structured, actionable insights. Root causes, evidence gaps, and documentation weaknesses are surfaced across claims for proactive intervention, targeted documentation improvements, and systematic denial prevention.

What Sets ClaimIQ Apart

ClaimIQ is not a general-purpose AI applied to healthcare. It is purpose-built: trained on proprietary claims data across hundreds of payers and providers, with contract parsing logic refined against real payer-provider agreements, and clinical reasoning grounded in actual medical record review workflows.

The combination of payer-specific training data, multi-modal document understanding, and continuous model improvement creates performance advantages that compound over time and are difficult to replicate.

1 DocIQ

The platform functions as an AI-assisted appeal analyst. It reads and interprets the full data set of a UB-04 claim form—patient and facility details, diagnoses, procedures, charges—and presents findings alongside plain-language field descriptions.

Aspirion’s team can query DocIQ directly about any document and receive detailed, cited responses. When generating appeal letters, it draws on medical records, clinical guideline criteria, and supporting documentation to construct a complete, claim-specific appeal.

UB-04 Reading
DocIQ reads and interprets all critical data points from hospital billing claim forms—patient details, diagnoses, procedures, and charges—presented with clear field descriptions for clinical and billing staff.
Medical Record Analysis
Answers questions about patient history, admission rationale, and clinical findings—citing the specific location in the record where each answer was found. Aspirion can ask follow-up questions directly.
Appeal Letter Generation
Generates thorough, claim-specific appeal letters that incorporate medical record evidence, clinical guideline criteria, and supporting documentation into a structured, persuasive narrative.

2 ContractIQ

The platform transforms complex payer agreements into structured, queryable rule databases—enabling accurate expected reimbursement calculations across commercial payers.

Accurately pricing a claim often requires pulling from multiple sources simultaneously. ContractIQ weaves together claim data, Medicare fee schedules, CMS weight tables, and payer-specific inputs to produce a defensible reimbursement calculation—even for the most complex claims.

Structured Contract Intelligence
Payer-provider agreements and addenda are parsed and organized into a database capturing facilities, key terms, effective dates, plan names, and fee schedules—enabling systematic, commercial claim pricing based on actual contract terms rather than estimates.
Multi-Source Reimbursement Calculation
For commercial claims, ContractIQ calculates expected reimbursement by combining claim data with external pricing inputs, Medicare fee schedules, CMS weight tables, and other payer-specific sources—alongside commercial payers with modeled contracts.

3 ClinIQ

ClinIQ translates denial outcomes into clear clinical insight, revealing where documentation and evidence fall short so teams can intervene earlier and prevent denials before they occur.

Clinical Signal Analysis
Clinical data from denied claims is analyzed against evidence-based criteria to assess documentation strength, identify missing or insufficient support, and highlight gaps impacting claim outcomes.
Denial Pattern Intelligence
Trends across diagnoses, payers, and denial reasons are aggregated to surface high-impact patterns and prioritize areas for intervention.
Proactive Improvement Enablement
Insights feed directly into clinical documentation improvement (CDI) workflows, enabling CDI teams to target high-risk cases, close documentation gaps earlier, and help prevent repeat denials before claim submission.