dQM - Measures for HEDIS^®

Using HL7 FHIR and Clinical Quality Language (CQL), both administrative (claims) and clinical (from EMRs) data are ingested and normalized so machine-readable measure logic is applied. Chart chases, manual abstraction, and spreadsheet-driven reconciliation are eliminated as clinical context, terminology and semantic harmonization across the systems is maintained. Scores are generated directly from computable data with traceable logic, producing results that highlight care gaps and risk, and are explainable for audit response.
 

Audit-ready outputs with traceability and less manual effort and abstraction.

dQM - Measures for HEDIS provides consistent, standards-based results that both payers and providers can collaborate on. Using CQL-based logic and traceable data quality teams can measure, monitor, and act more continuously, with fewer disputes about ‘how the rate was calculated’. This creates more holistic collaboration between Payers and Providers, as well as enhances member satisfaction, which is directly linked to STAR Ratings. Transparent logic and traceable data reduce disputes, simplify audit response, and support cross-organization alignment.

One collaborative computable measurement layer. Fewer disputes. Stronger payer–provider alignment.

The combined power of a FHIR-first CQL engine transforms NCQA’s Digital HEDIS content into sharable, computable formats that reduce manual interpretation and variability and support more timely quality improvement actions. Instead of relying solely on retrospective reconciliation, teams can identify prospective care gaps and prioritize interventions sooner, supporting value-based care with reduced administrative burden.

Quality insights that can influence outcomes and support STAR Ratings improvement efforts.

As NCQA evolves measures, static, proprietary hard-coded logic can increase duplication of effort and technical debt. OmniQ - dQM Measures for HEDIS supports digital measurement approaches that can extend to other quality programs (for example, MIPS, STAR Ratings, Medicare, Medicaid, custom and state initiatives), enabling rapid updates as regulatory requirements and measure logic evolve. Quality teams respond to change without re-engineering workflows or rebuilding logic each measurement year, supporting modernization and innovation efforts well beyond 2030.

Implement updates in weeks, not months, without disrupting operations.