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

1. 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.

• 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.

• 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.