dQMs - Digital Quality Measures

​Digital Quality Measurement in a Learning Health System

Data standardization and interoperability support digital quality measurement. In digital quality measurement, the data used are digital, standardized, and a seamless outgrowth of data generated from routine workflows. Data sharing is standards-based to maximize interoperability, minimize burden, and facilitate the development and use of common tooling across use cases. This approach supports data analysis, rapid-cycle feedback, and quality measurement that are aligned for continuous improvement in patient-centered care. As interoperability standards and technology evolve, quality information could become available in near real-time, aiding in rapid improvement to patient care.

Once digital data can be captured, validated, shared, and merged for analysis, CMS will be poised to contribute to the promise of a learning health system that supports improved quality of patient care. In a learning health system, data are leveraged to inform multiple use cases, including quality measurement, improvement, and public health. The graphic shows the data cycle includes

• surveillance activities to derive evidence from the data
• translating evidence to clinical guidelines and clinical decision support
• using data and guidelines to transform clinical care
• conducting quality improvement activities
• interpreting and applying the data to support measurement and analytics
• reporting out results based on data

These actions are bi-directional and will lead to improved patient outcomes and the delivery of high-quality care for patients. CMS will continue to explore avenues for leveraging interoperable tools that facilitate impactful digital data and information sharing.

CMS Digital Quality Measures

CMS defines digital quality measures (dQMs) as quality measures that use standardized, digital data from one or more sources of health information that are captured and exchanged via interoperable systems; apply quality measure specifications that are standards-based and use code packages; and are computable in an integrated environment without additional effort. The set of solutions for dQMs enables querying the data needed from standards-based application programming interfaces (APIs) (such as Fast Healthcare Interoperability Resources [FHIR®] APIs), calculating the measure score, and generating outputs necessary for quality reporting, that also support quality improvement efforts. dQMs improve patient care and experiences by ensuring patient and provider access to necessary information, improving the quality of care, the health of populations, and reducing costs due to the rapid-cycle nature of digital health data and dQM calculation.

The next graphic visualizes digital quality measures and how they can contribute to a learning health system. Structured digital health data are the building blocks (i.e., Lego®) for quality measures. In accordance with the dQM definition, the provenance of this data can be from one or more sources, which must be cleaned and validated for use. Standardized data exchanged via FHIR can be aggregated and used to calculate the specified quality measure (i.e., Lego house) providing insights to the health care ecosystem (i.e., Lego neighborhood).

Digital Data Sources

As depicted in the graphic, some examples of digital data sources for dQMs are

• administrative systems
• electronically submitted clinical or social needs assessments
• electronic health records (EHRs)
• laboratory systems
• prescription drug monitoring programs
• instruments (for example, medical devices and wearables)
• patient portals or applications (for example, for collection of patient-generated health data such as home blood pressure monitoring or patient-reported health data)
• health information exchanges (HIEs)
• clinical registries

The FHIR Standard for CMS Digital Quality Measures

Electronic clinical quality measures (eCQMs), which typically use EHR data, may be refined or repackaged to better fit within the dQM umbrella. Operationally, the first step in CMS’s transition to dQM is to transition current CMS Quality Data Model (QDM)-eCQMs to CMS FHIR eCQMs. FHIR eCQM reporting will leverage standardized data and the FHIR model. It can serve as a model for how future digital reporting can occur to ultimately improve patient experience and quality of care. By revisiting existing value sets, data standards, measure logic and specifications and shifting specifications to the FHIR standard, CMS will pave the way for additional structuring of future, broader digital-based quality measures.