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How Digital Measures Execute with Clinical Quality Language

Learn more about CQL engines, how they work and why they are important for digital quality.

HEDIS Digital Measures and CQL

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Developed and maintained by NCQA, HEDIS® (the Healthcare Effectiveness Data and Information Set) is a comprehensive set of standardized performance measures that play a crucial role in promoting high-quality, patient-centered care by providing a reliable, standardized and efficient framework for assessing and improving health care performance. “HEDIS digital measures” refers to the digitalization of these measures, allowing them to be interoperable with sources of digital health data, such as EHRs, claims systems and other systems at an organization.

Importance of CQL in HEDIS

To facilitate quality measurement and enable benchmarking and comparison across systems and regions, it is important that health care organizations be able to communicate through a common language that expresses clinical concepts and related logic clearly and consistently, and that can be read and understood by both humans and machines.

CQL plays a critical role in helping health care systems maintain high-quality care by providing a standard way to express clinical knowledge, assess quality, support interoperability and serve as the foundation for creating automated processes that guide clinical decisions.

The Takeaway…

Digital measures are standardized metrics used to assess aspects of health care quality such as patient outcomes, processes and service utilization.
CQL provides a consistent approach to defining clinical quality measures, ensuring that digital measures are uniformly interpreted and implemented.

How CQL Engines Work

A CQL engine is a software system designed to understand and follow instructions written in Clinical Quality Language. The engine processes CQL expressions to evaluate clinical data and perform automated health care-related tasks. Clinical data are input into the CQL engine, which uses the instructions in the CQL script to compute quality measures, apply clinical decision rules and derive outcomes.

The interaction of digital measures with CQL engines, along with efficient data retrieval, calculation and aggregation, forms the backbone of accurate, effective quality assessment and reporting in health care:

  • Translation: Digital measures are written in CQL to standardize the logic used to assess quality metrics. This involves translating measure definitions, criteria, and calculation rules into CQL expressions (i.e., any statement that returns a value).
  • Execution: The CQL engine processes these expressions against data from EHRs or other data sources to identify patient cohorts, calculate metrics, and evaluate performance.
  • Feedback: Execution results provide feedback on quality measures, often in the form of dashboards, reports, or direct integration into EHR systems.

Benefits of Using CQL for Digital Quality Measures

Standardization

CQL provides a standardized way to define and compute quality measures, ensuring consistency across health care systems and settings

Automation

CQL engines reduce manual errors and improve efficiency of quality assessment processes.

Accuracy

Appropriate and consistent application of CQL logic ensures that quality measures are accurately calculated, supporting reliable reporting.

Compliance

Automated and standardized quality assessments help health care organizations meet regulatory requirements and participate in quality improvement programs.

Data Flows and Operations in CQL-Based Systems

1

Ingestion

Patient data are ingested, normalized and transformed into a common format to ensure compatibility with CQL expressions.
2

Retrieval

CQL queries are formulated and executed against the data repository to extract relevant patient records, conditions, treatments and outcomes.
3

Calculation

The CQL engine processes retrieved data according to the logical definitions provided in the CQL script.
4

Aggregation

Individual patient results are aggregated to generate overall performance metrics
5

Reporting

Aggregated data are compiled in reports and dashboards for stakeholders, including health care providers, administrators and regulatory bodies.

Advantages of Configurability in CQL Content

“Configurable CQL content” refers to the ability to modify, extend or customize CQL-based logic and expressions to meet needs or requirements of health care organizations without altering the core structure of the language. This allows health care providers to tailor the clinical logic to fit their unique workflows, patient populations and quality improvement goals.

Customization

  • Adapt to specific needs: Health care organizations can adjust CQL logic to reflect specific clinical practices, patient demographics, and local guidelines.
  • Personalized measures: Customizable content allows the creation of personalized quality measures that are relevant to an organization's goals and patient population.

Flexibility

  • Evolving standards: Organizations can update CQL logic to quickly adapt to changing clinical guidelines, regulations or quality standards.
  • Scalability: Configurable content supports scalability as organizations grow or change service offerings.

Improved Accuracy

  • Precise measurement: Tailored measures accurately capture intended clinical actions and outcomes, leading to more precise quality assessments.
  • Reduced errors: Custom configurations help reduce errors that can occur from using generic or non-specific measures.

Compliance and Reporting

  • Regulatory alignment: Organizations can ensure their quality measures align with local, state or federal regulations by customizing CQL content to meet specific requirements.
  • Better reporting: Customizable content facilitates accurate and meaningful reporting, helping organizations meet reporting requirements and achieve better performance in quality programs.

Use Cases for CQL Content

CQL use cases cover clinical decision support, quality measurement, population health management, clinical research and regulatory reporting. CQL’s configurability, alignment with health care standards and support for interoperability make it a critical tool for ensuring high-quality, compliant, efficient health care delivery.

Clinical Decision Support

Clinical Decision Support

  • Real-time alerts: Implement clinician alerts based on patient data, to prompt timely interventions (e.g., reminders for preventive screenings).
  • Guideline adherence: Provide decision support to ensure adherence to clinical guidelines and best practices during patient care.
Quality Measurement

Quality Measurement

  • Performance metrics: Calculate performance metrics for quality measures, such as those defined by CMS or NCQA.
  • Patient outcomes: Measure and analyze patient outcomes to identify areas for improvement.
Population Health Management

Population Health Management

  • Risk stratification: Identify high-risk patients for targeted interventions using data-driven criteria.
  • Chronic disease management: Monitor and manage chronic diseases by tracking relevant metrics (e.g., HbA1c levels in patients with diabetes).
Clinical Research

Clinical Research

  • Cohort identification: Define patient cohorts for clinical trials based on specific inclusion/exclusion criteria.
  • Data extraction: Extract relevant clinical data for research using standardized queries.
Regulatory Compliance

Regulatory Reporting

  • Compliance reporting: Generate reports to meet regulatory requirements, such as those mandated by MACRA (Medicare Access and CHIP Reauthorization Act) or MIPS (Merit-based Incentive Payment System).
  • Accreditation: Support data collection and reporting for accreditation by NCQA or other entities.

Writing CQL Statement

Implementation for measuring clinical quality includes three parts:

  • Metadata: Describes details about the tool: its ID, version, status, health care topics it covers, related tools, different languages, authors… and more.
  • Clinical quality information: The clinical data used to calculate quality indicators.
  • Expression logic: The core part of the tool. The conditions or rules that analyze data to calculate quality indicators.

In the example below, CQL is used to develop an indicator in an asthma patient’s medical record that a Home Management Plan of Care document was given to the patient/caregiver.

example CQL

Best Practices for Creating Effective CQL Statements

CLARITY AND READABILITY

Descriptive Naming: Use clear, descriptive names for definitions and functions to improve readability and maintainability.
Comments: Add comments that explain complex logic or implementation details.

MODULARITY

Descriptive Naming: Use clear, descriptive names for definitions and functions to improve readability and maintainability.
Comments: Add comments that explain complex logic or implementation details.

PERFORMANCE OPTIMIZATION

Efficient Queries: Write efficient queries to minimize performance impact, especially when working with large datasets.

VALIDATION AND TESTING

Validation Tools: Use CQL validators and translators to check for syntax and logical errors.

ADHERENCE TO STANDARDS

Consistent Data Models: Align with standardized data models (e.g., FHIR) to ensure interoperability.
Guideline Compliance: Ensure that CQL logic aligns with clinical guidelines and quality measure specifications.

DOCUMENTATION

Detailed Documentation: Maintain comprehensive documentation for each CQL library and its components to facilitate understanding and future maintenance.
Version Control: Use version control systems to manage changes and maintain a history of updates to the CQL code.

Open-Source Resources

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Open-source resources can be leveraged to create effective, efficient, maintainable CQL statements that enhance the quality and interoperability of health care data and processes; for example:

  • CQL Libraries and Repositories
    • FHIR Clinical Guidelines Implementation Guide
    • A multi-stakeholder effort to use FHIR resources to build shareable, computable representations of clinical care guidelines. Repositories (e.g., the FHIR Clinical Guidelines repository) provide CQL examples that demonstrate best practices and common use cases.
  • Measure Authoring Development Integrated Environment (MADiE)
    • Provides the capability to express complex measure logic and export measures in several formats, including:
      • A human-readable document that can be viewed in a web browser.
      • An eCQM HQMF XML document that provides metadata, terminology, data elements and specific population definitions respective to the measure.
      • A CQL file containing the terminology and expression logic used by the measure.
      • An Expression Logical Model (ELM) XML export that is a computer-readable XML version of the CQL file.
      • A JavaScript Object Notation (JSON) export, which is a serialized format of the ELM file. The data expressed in the tool are the input for creating defined export files.
  • Online Editors and Validators
    • CQL-to-ELM Translator
    • Translates high-level CQL syntax into the ELM representation. Used in support of Clinical Quality Framework implementations to enable CQL output to be uniformly and automatically translated into ELM XML or JSON documents to support implementation, integration, translation and execution of CQL-based artifacts (i.e., items produced during the development process).
  • Community Support
    • Forums and discussion groups: For example, HL7 CQL community forums, which provide support and share knowledge among CQL developers.
    • Open-source projects: Contribute to and learn from open-source projects on GitHub where CQL is utilized (e.g., the CDS Connect repository).

Software Requirements for Running Digital Quality Measures

Executing digital quality measures in health care requires a robust infrastructure comprising CQL engines, ETL tools, data warehouses and user interface/reporting tools. These components must be compatible with data sources such as EHRs and claims systems, and must use standardized data models and APIs for seamless data access and integration

Key Software Components for Executing Digital Quality Measures

User Interface and Reporting Tools

Provides dashboards, reports and other visualization tools for monitoring and analyzing performance metrics.

CQL Engine

Interprets and processes CQL logic against patient data to identify cohorts, perform calculations and generate results.

Data Warehouse/Repository

Stores patient data and other information in a structured format at a centralized location, facilitating efficient retrieval and processing by the CQL engine.

Data Extract, Transform, Load Tools

Extracts data, transforms it into a suitable format and loads it into the data warehouse or repository.

Interoperability and Integration Middleware

Ensures seamless integration of data from EHR systems, HIEs and other sources into the quality measurement framework.

Ensuring Compatibility With HEDIS Measures and Health Care Standards

Standardized Data Models

Ensure the CQL engine supports standardized data models like FHIR, which are commonly used in HEDIS measures and other health care standards. Implement mappings and transformations where necessary, to align data from different sources with these models.

Measure Specifications

Obtain the latest specifications for HEDIS measures; ensure the CQL engine can interpret and execute them accurately. Regularly update measure definitions to reflect changes in HEDIS specifications.

Interoperability

Validate data exchange processes to ensure accurate and consistent data transfer between systems.

Testing and Validation

Conduct thorough testing using test cases based on HEDIS measures and other relevant standards. Validate results against expected outcomes to ensure accuracy and reliability.

Digital Content Services Offers a Streamlined Approach

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For organizations that report HEDIS measures, Digital Content Services provides a delivery mechanism that allows out-of-the-box configurability of measures and reduces costs and burden associated with certification processes, in addition to eliminating the need to interpret and program measures based on specifications in HEDIS Volume 2: Technical Specifications for Health Plans.

Digital Content Services is NCQA’s first software application for digitized HEDIS measures, hosted in a cloud environment. NCQA provides comprehensive support and training to help organizations leverage the full potential of Digital Content Services.

Key Considerations for Implementing CQL Engines

When integrating/using CQL execution engines in a health care setting, asking the right questions can ensure successful deployment and operation.

What are the specific clinical quality measures and use cases?

Identify quality measures and clinical decision support rules that need to be implemented. Determine how these measures align with organizational goals and regulatory requirements.

What is the existing technical infrastructure?

Assess the current IT infrastructure, including EHRs, data warehouses and other relevant systems. Determine how the CQL engine will integrate with these systems.

What data sources will be used?

Ensure that the engine complies with relevant regulations and standards Implement robust security measures to protect patient data.

What are the security and compliance requirements?

Conduct thorough testing using test cases based on HEDIS measures and other relevant standards. Validate results against expected outcomes to ensure accuracy and reliability.

How will the CQL engine be maintained and updated?

Develop a plan for maintaining and updating the CQL engine. Determine who will be responsible for these tasks, and the processes that will be followed.

What training and support will be needed?

Identify the training needs for staff who will use and maintain the CQL engine. Ensure adequate support is available to address issues and answer questions.

Integrating HEDIS Digital Measures With CQL Engines

By evaluating key factors and implementing effective solutions, health care organizations can successfully integrate CQL engines to execute HEDIS measures and other digital quality measures, ensuring accurate, efficient and compliant quality assessment and reporting. The figure below outlines common challenges and potential solutions to mitigate risk.

FACTORCHALLENGESOLUTION
MEASURE LOGICImplementing the complex logic of HEDIS measures accurately in CQL.Conduct comprehensive testing to validate the logic; collaborate with clinical experts to ensure accuracy.
DATA SOURCESIntegrating data from diverse systems with different data standards and protocols.Leverage interoperability standards like FHIR, and implement middleware solutions to facilitate data exchange.
PERFORMANCEExecuting HEDIS measures on large datasets can strain system resources.Optimize CQL queries for performance; ensure adequate computational resources are available.
REGULATORY COMPLIANCEEnsuring compliance with regulatory requirements while integrating HEDIS measures requires due consideration.Implement robust security and privacy measures, conduct regular compliance audits and ensure effective oversight.
CONTINUOUS UPDATESKeeping current with updates to HEDIS measures and other standards, which can get sidelined due to other priorities.Establish a process for continuous monitoring and timely updates of measure definitions and CQL logic.

FHIR® is the registered trademark of Health Level Seven International and use does not constitute endorsement by HL7.

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