Care Evolution

Introduction: The digital quality promise… amplifying the health data quality challenge

For years, reporting for measures contributing to CMS Star Ratings has been a cornerstone of quality assessment, often perceived as a necessary but retrospective, burdensome task. The shift towards digital quality measures (dQMs) – defined using standards like Fast Healthcare Interoperability Resources (FHIR®) and Clinical Query Language (CQL) – promises much more than streamlined reporting. It holds the potential to transform quality measurement from a look-back exercise into a dynamic engine for proactive, data-driven Quality Improvement (QI). Like you, we believe quality measurement should be more than just a retrospective ‘batch’ reporting exercise; it should be a catalyst for care as it’s needed not months later.

For Quality and IT teams alike, the vision of migrating to real-time Quality Measurement is exciting and transformational. Low quality data remains a challenge for current Quality reporting.  The move to Digital Quality Measures only serves to amplify the data quality and format problem that plagues the industry.  

A digital solution approach: mastering data quality before measurement & action

So, how can organizations move quality measurement from a reporting burden to a proactive 360 degree improvement process? What we’ve seen work effectively often involves more than just a calculation tool. Success relies on a modular platform approach architected to tackle those upstream data quality challenges head-on. Thinking about what capabilities might be essential, based on solving these complex data puzzles, consider these elements:

1. Data ingestion management: Securely ingesting diverse data is foundational, including robust aggregation and sophisticated patient matching to build accurate longitudinal records while maintaining strict data provenance for auditability. If you already have a data ingestion solution, you can also use our modular data cleansing tools to clean the data after it has been ingested to make it ‘digital quality ready’.
2. Intelligent standardization & enrichment (the core differentiator): This is where raw, messy data becomes reliable, computable intelligence. It involves:
   • Advanced terminology mapping: Leveraging sophisticated services to map disparate local codes, proprietary formats, and free text concepts to required standards (SNOMED CT®, LOINC®, RxNorm®, ICD-10), ensuring semantic consistency crucial for accurate measure logic execution.
   • Unlocking unstructured data: Applying proven, healthcare-specific Natural Language Processing (NLP) and responsible AI/ML capabilities, like those embedded within sophisticated data platforms, to extract structured, coded meaning (e.g., diagnoses, medications, procedures, findings) from clinical notes, PDFs, and faxes. This significantly enriches the data available and improves data quality before it’s transformed into FHIR for measurement. For example, imagine automatically extracting and standardizing poorly documented HbA1c control levels from scanned specialist notes using NLP, making that crucial data available for diabetes care gap measures (a common challenge we can help solve).
3. Rigorous, quality-aware FHIR® transformation: Transforming the now consolidated, standardized, and enriched data into the required FHIR R4 formats and specific profiles (e.g., US Core, CARIN BB, HEDIS Core IG). This isn’t just format shifting; it’s encoding high-quality, computable data into the standard, ready for reliable measure execution.
4. Secure & auditable infrastructure: Operating within a certified framework (HITRUST e1, NIST SP 800-53) ensures data integrity, protects privacy, and builds the necessary trust in the entire data pipeline and the insights produced.
5. Actionable workflow integration (enabled by trust): Pushing dQM-derived insights (care gap alerts, risk indicators, quality opportunities) directly into clinicians’ native workflows within EHR systems (like Epic and athenahealth) via proven bi-directional exchange capabilities. This ‘last mile’ only delivers transformational value when the insights are timely and demonstrably trustworthy, stemming directly from a high-quality, transparent data foundation.

Passing the raw patient data through the Convert API to standardize the medical records and uplift data elements to feed higher quality data to dQM services.

Why this quality centric approach matters: Unlocking proactive Quality Improvement for STAR Ratings & beyond

By integrating certified measure logic (like HEDIS dQMs from NCQA’s DCS) after establishing a data pipeline that prioritizes solving the upstream data quality challenges, organizations like yours will finally be able to:

• Enable trustworthy, proactive quality improvement for STAR Measures: Shift focus from retrospective reporting on potentially flawed calculations to identifying and closing real care gaps impacting STAR Ratings in near real-time, based on data you can depend on. Imagine identifying and addressing verified care gaps before the reporting cycle even ends.
• Reduce provider burden & enhance engagement: Deliver actionable and trusted insights directly into the EHR workflow where clinicians already work. This minimizes context switching, reduces portal fatigue, freeing up valuable clinician time and reducing frustration, and increases the likelihood of timely, appropriate action because the information is reliable. We hear frequently from clinical leaders that reducing this friction is paramount, and it’s only possible when the insights presented are trusted.
• Improve foundational data quality: Leverage advanced terminology mapping and NLP not just for measures, but to create a higher fidelity, longitudinal data asset for all analytics. This strengthens the foundation for more accurate Star calculations, sophisticated risk adjustment, value-based care analytics, and broader business intelligence.
• Build strategic, future-ready infrastructure: Establish a quality-centric, data platform that not only supports today’s dQMs but also accelerates readiness for the entire ecosystem of interoperability initiatives. This includes CMS mandates like the Patient Access, Provider Access, Payer-to-Payer APIs, and crucially, the upcoming Prior Authorization API, which relies on similar principles of standardized data exchange and automation. This foundational approach serves both early adopters seeking competitive advantage and organizations needing efficient pathways to meet upcoming compliance deadlines, reflecting the diverse needs we encounter among the health plans and provider groups we work with.

CareEvolution®: Partnering to bridge the digital quality gap for quality measures

CareEvolution aims to be a partner in overcoming the data quality barriers that can hinder effective dQM implementation and Star Ratings improvement. Our Orchestrate platform provides capabilities focused on comprehensive data management, industry-leading standardization and enrichment (including sophisticated NLP and terminology services that turn raw data into reliable FHIR), secure data transformation, and proven bi-directional workflow integration that brings insights to the point of care.

While sources like NCQA’s DCS provide essential HEDIS measure logic, our focus is on tackling the prerequisite challenge: ensuring the FHIR data feeding that logic is accurate, complete, standardized, and trustworthy. This focus on solving the upstream data quality problem first aligns with the primary quality improvement goals we hear from organizations like yours, whether you’re forward thinking early adopters or focused on efficient compliance.

Conclusion: The future is digital and actionable in real-time, fueled by high quality data

The evolution to dQMs using FHIR® and CQL is more than a technical upgrade; it’s a strategic imperative for improving care and CMS Star Ratings performance. However, success hinges on addressing the often-overlooked foundation: data quality. By investing in a robust technology that excels at transforming messy, real-world data into high-quality, standardized FHIR – and then seamlessly integrates the resulting trustworthy insights into clinical workflows – healthcare organizations can finally bridge the gap between measurement and meaningful improvement. It’s time to move beyond just calculating measures and start driving reliable action. Let’s work together to remove the data quality barrier and unlock the true potential of digital quality.