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Supporting Learning Health Systems With CMS dQMs
Article

Supporting Learning Health Systems With CMS dQMs

The Centers for Medicare & Medicaid Services (CMS) has been on a path to move from paper-based capture of quality measures to electronic clinical quality measures (eCQMs) toward digital quality measures (dQMs). Their goal is to not only capture all quality measures digitally, but to create standards and a repository that would go beyond reporting to sharing healthcare data related to quality improvement use cases. Ultimately, their objective is to support learning health systems (LHS) to optimize patient safety, outcomes, and experience.

But can dQMs and the CMS roadmap help provider organizations deliver more excellent healthcare? We provide a roadmap update, outlining the benefits to those organizations that are advancing on the “learning health system " path.

What are dQMs and eCQMs?

dQMs, or digital quality measures, are organized as self-contained measure specifications and code packages. They may use one or more sources of health information and are transmitted electronically via interoperable systems.

CMS quality measures have two purposes: to promote quality and reduce waste, and to improve decision-making. They specifically do this through incentivizing good performance and disincentivizing poor performance via public reporting and value-based payment programs. These measures support clinicians and hospitals in tracking their performance and in surfacing public data that can be used to make decisions like how to launch a health equity strategy or population health program.

Quality measures have been evolving from paper-based collection via claims, manual chart extractions, and patient surveys to electronic clinical quality measures (eCQMs). This quality data is primarily garnered from electronic health records (EHRs). Ultimately, the objective is to reduce administrative burden and improve timeliness of feedback to providers on quality of care by collecting all quality measures from digital sources, including data from EHRs, registries, health information exchanges (HIEs), claims and surveys.

What is CMS's goal for digital quality measurement and supporting learning health systems?

This new course, transitioning to digital quality measures, is part of a larger objective to support provider organizations in becoming learning health systems (LHS). Through technologies like Fast Healthcare Interoperability Resources (FHIR®) and application programming interfaces (APIs), coupled with data standardization, hospitals and health systems can streamline data capture, sharing, and quality measure management. Additionally, they can enhance quality improvement initiatives and support clinical decision-making through a broader cohort of quality and performance data across organizations.

Access to a broader ecosystem of quality and performance information supports each provider organization's unique data analysis and quality improvement goals. CMS's goal is to make that continuous cycle of quality improvement easier and more robust, which holds promise to improve all healthcare quality in the U.S. Learning health systems deliver high-quality patient care utilizing rapid-cycle feedback and continuous improvement, usable and timely data from multiple sources, and reliable and valid measurement.

How does dQM support CMS's national quality strategy?

CMS developed a strategic roadmap for advancing digital quality measurement centered around four domains:

  • Advance technology
  • Enable measure alignment (including measures, data, and tools)
  • Improving data quality
  • Optimizing data aggregation

The dQM roadmap supports CMS's eight national healthcare quality strategy goals:

  • Embed quality across the care journey, extending quality across payer types
  • Advance health equity
  • Promote safety to prevent harm and death
  • Foster engagement with stakeholders with focus on person- and family-centered care
  • Strengthen resiliency in the healthcare system
  • Embrace the digital age
  • Incentivize scientific innovation and technology
  • Increase alignment to promote seamless and coordinated care
  • Foundational to dQM and these national goals is achieving data standardization

Data standardization is the first step towards dQM and creating Learning Health Systems

According to CMS, learning health systems generate knowledge from data captured during routine care. Data standardization transforms that data into a common format, ensuring quality, allowing data sharing, and supporting programmatic use of data.

While dQM focuses on quality measurement for performance reporting and analysis, CMS's goal is to use standardized data models to support broader interoperability of patient health data, healthcare data analytics, and research.

However, for provider organizations to achieve more structured and standardized data, they must overcome barriers to implementing dQMs. These include slow adoption of current standards, lack of provider data mapping and quality assurance of required data, as well as managing change in clinical workflows.

How could dQMs support learning health systems?

With greater provider adoption and standardization, dQM implementation can be seamless. Automated data extraction can be enabled through FHIR and using the United States Core Data for Interoperability (USCDI) standards, as well as supplemental standards like USCDI+. These facilitate valid and reliable data mapping, making data auditing easier. Advanced technologies also eliminate many workflow changes only requiring them for QI priorities.

dQM supports the learning health system

Health systems and provider organizations can benefit from CMS's goal to achieve greater interoperability and the capture and sharing of dQM in four ways:

  • Creating standards for sharing data from EHRs, registries, HIEs, claims, patient experience surveys, etc.
  • Giving them quality and other data for analysis from other provider organizations for delivering quality care, quality improvement, benchmarking and PHM
  • Streamline and reduce the burden of capturing and submitting quality measures when they are completely digital/electronic
  • Produce reliable and valid measurement results common across multiple programs and payers

Learning health systems need streamlined digital data capture across multiple sources to realize a data-driven enterprise of care. With it, provider organizations can collaborate with partners better, harmonize data for quality and performance analysis, and improve health outcomes.

Get ready for dQM to foster data-driven learning health systems

RTI Health Advance employs data scientists, clinical and healthcare operations experts, and technologies to support data-driven quality and performance initiatives. Contact us.

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