Clinical Data as An Asset to Transform HEDIS®

Background: HEDIS Quality Measures & The Digital Future

Background: HEDIS Quality Measures & The Digital Future HEDIS is used by more than 90% of health plans. Nearly 200 million people are enrolled in plans that report HEDIS results.¹ Billions of dollars are allocated based on HEDIS measures. Specifically,

• HEDIS scores reported to NCQA allows commercial plans to be compared to each other by consumers and employers.
• CMS Star Ratings are affected by HEDIS measures that assess the management of preventive care and chronic conditions. A Medicare Advantage plan’s 5-Star Rating determines bonus payments and rebates and is a competitive advantage.
• Some states require Medicaid and other plans to report HEDIS measures as a requirement for quality reporting and participation in value-based care programs.
• Health plans also use HEDIS measures in real time for quality improvement and to identify gaps in care, enabling them to make improvements prior to annual reporting.

For years, health plans relied solely on claims data to calculate HEDIS measures. The next generation of quality reporting involved hybrid measures, which used chart abstraction to glean clinical outcomes and supplement administrative data. Now, digital supplemental data from EHRs and other sources such as HIEs can be combined with claims to calculate performance on measures that require clinical information, such as test results or vital signs.

As an example, the HEDIS measure for Controlling High Blood Pressure (CBP) focuses on adults aged 18-85 with a diagnosis of hypertension, where their blood pressure is adequately controlled (<140/90 mmHg). Standard diagnosis codes determine the denominator hypertensive population, while compliance in the numerator population is determined using standard vital sign codes and results. Vital signs can be recorded using CPTII codes like 3074F, which indicate systolic blood pressure <130 mmHg, or specific readings gathered from clinical sources.

Incorporating accurate, standards-adherent clinical information in quality measurement ensures the correct representation of closed gaps. If a healthcare provider uses a local, site-specific code to document evidence of hypertension in the medical record, the diagnosis may not be captured automatically in quality reporting. As a result, the patient may be excluded from the denominator of the above measure. This means the member is not only excluded from the HEDIS measure, representing it inaccurately, but they may be excluded from necessary disease management programs and excluded from care gap outreach workflow.

Over time, this one data gap can lead to poor health outcomes, low engagement, as well as avoidable cost and utilization. Another example is when a member with a hypertension ICD10 code diagnosis is included in the denominator, but their recent blood pressure readings are recorded in the EHR without units. This means the vital sign results cannot be used in the quality measure calculation and compliance is under-reported. Further, because the member is included in the denominator, but appears to have an open care gap, they may be incorrectly included in outreach, causing unnecessary workflow burden, duplicate services, and waste. This results in poor member experience and their engagement and satisfaction may decline over time.

Both CMS and NCQA have initiatives that are propelling the industry towards a digital quality future in which clinical data captured in EHRs can be reported once and used for multiple purposes. This will eliminate continuous requests for charts from payers to providers as well as dependence on manual chart abstraction, removing burden, cost, and time from quality management processes. Quality reporting will also be aligned with interoperability and data exchange standards, such as Fast Healthcare Interoperability Resources (FHIR.), which are being implemented across the industry for further efficiency.

Example initiatives include:

• NCQA calls out three pillars for the future of HEDIS: Better data, more standardized measure calculations, and patient-specific measures focused on outcomes.. “Better data” means that leveraging tools such as Availity Fusion will be critical.
• CMS, supported by NCQA, has announced a goal that all measures be Digital Quality Measures (dQMs) by 2025.. dQMs provide measure specifications in digital format that can be loaded into IT systems, so they can be more consistent and less subject to human error. dQMs are defined using Clinical Quality Language (CQL) and FHIR, which aligns quality measurement with interoperability initiatives spearheaded by CMS for patient access, payer to payer data exchange, and prior authorization. The goal is to have discrete data in FHIR available to push downstream for any entity (CMS, NCQA, participants in value-based care).
• NCQA’s Electronic Clinical Data Systems (ECDS) reporting standard is an alternative to traditional HEDIS reporting, that leverages multisource clinical data from EHRs, HIEs, and registries and encourages interoperability and use of standards. ECDS measures often require clinical data such as blood glucose level test results. ECDS reporting has been voluntary but will be required for some measures in 2022 and 2023. For example, Breast Cancer Screening (BCS) and Colorectal Cancer Screening (COL) measures are moving to ECDS-only reporting for measurement year 2023, and measurement year 2024, respectively.
• The NCQA Data Aggregator Validation program allows HIEs and aggregators to validate digital data streams that can be used as standard supplemental data for HEDIS reporting. This program encourages the use of clinical data because it validates data upstream rather than at each health plan, lowering the audit burden.

The Mechanics of Clinical Data Consumption & HEDIS Extracts

Availity Fusion supports data upcycling by aggregating data from EHRs, HIEs, labs, and other sources and then normalizing and enhancing it to deliver a consistent, standards-based data asset that’s ready for use. For HEDIS reporting and care gap identification, the data extraction pipeline pulls data from the Availity Fusion engine, filters for relevant data elements, and produces flat files for ingestion by HEDIS reporting engines. Availity’s HEDIS Extracts are a fit for purpose output, designed to increase accuracy and efficiency across HEDIS measurement by reporting engines. HEDIS engines can then calculate, measure, and submit quality compliance rates by processing data from clinical and claims sources. The data must be delivered in specific layouts using standard value sets, where these value sets are defined as groups of one or more standard clinical codes, such as LOINC, ICD-10-CM, SNOMED-CT, CPT, etc.

Conclusion

Today, health plans are going beyond simply paying for services. They are actively involved in orchestrating the best care at the lowest price for members. Upcycled clinical data contributes to more accurate care gap identification and clinical quality measurement in support of this expanded role. But the shift towards strategic use of clinical data for HEDIS requires an investment in clinical data acquisition and – equally important – an investment in technology to transform raw clinical data into an asset that is standard, organized, and actionable. The inherent variation in source documentation and accuracy, a plethora of custom and standard terminologies, and fragmented storage of data across the healthcare system all contribute to clinical data’s complexity.

Availity Fusion technology normalizes data to industry standard terminologies, consistent with the HEDIS value set, and outputs fit for purpose HEDIS Extracts to increase the accuracy of quality measurement. With a higher volume of usable data available for reporting and care gap identification, compliance and health outcomes improve in tandem.