March 4, 2020
Donald Rucker, MD
National Coordinator for Health IT
U.S. Department of Health & Human Services
330 C St. SW 7th Floor
Washington, DC 20201
Dear Dr. Rucker:
Thank you for the opportunity to comment on the Office of the National Coordinator for Health IT’s (ONC) draft 2020–2025 Federal Health IT Strategic Plan. The overall goals, objectives and strategies in the draft plan can advance efforts by the National Committee for Quality Assurance (NCQA), the Centers for Medicare & Medicaid Services (CMS) and others to move to digital quality measures (dQM).
dQMs differ from traditional quality measures that rely on claims or arduous manual chart abstraction and electronic clinical quality measures (eCQM) that rely primarily on electronic health records (EHR) and may not provide a full picture of a patient’s health or might store data in inaccessible ways. Instead, dQMs use data recorded (in EHRs, registries and other electronic sources) in the normal course of care delivery. Moving to dQMs is essential to reduce burden, improve accuracy, produce more clinically relevant knowledge and measure what matters using rich data collected from multiple sources. We appreciate the draft’s call for optimal eCQM use, because we see this as a directional signal for next-generation eCQMs—dQMs, which have broader scope and utility.
We especially applaud the draft plan’s emphasis on promoting interoperability and data sharing through widely accepted standards. This is essential for the comprehensive digital ecosystem dQMs require. NCQA’s approach builds on widely used standards, including Health Level Seven’s (HL7) Fast Healthcare Interoperability Resources (FHIR), Clinical Quality Language (CQL) and Quality Reporting Document Architecture (QRDA), as well as on ONC’s Common Clinical Data Set (CCDS). There is growing consensus on use of these standards, and we encourage you to cite them, as appropriate, in the final plan.
Also, proposed ONC and CMS rules for implementing 21st Century Cures Act provisions to improve interoperability and end data blocking are essential for the interoperable data sharing dQMs require. We strongly encourage you to maintain the proposed rules’ strong policies on these issues so dQMs can properly function.
dQMs also can support your draft’s call to optimize quality and outcomes assessment and customize care through precision medicine to assist diagnosis and treatment through real-time data use. dQMs can measure more of what really matters, because the specifications use the data stored in electronic records, as well as data found in claims, to create a full picture of a person’s health care. dQMs hold the promise of measuring quality in ways that consider the patient and are based on the patient’s risk, rather than on broad guidelines meant for the overall population.
This requires the ability to obtain data on a patient’s demographics, clinical risks and social determinants, and we encourage you and other standards-setting bodies to include such information as data exchange standards evolve.
The draft plan’s call for increased use of new technologies and analytic approaches such as machine learning and predictive modeling can also play a critical role in moving to more person-centered measurement.
Progressing to dQMs can further support optimal quality by providing more timely information than traditional quality measures, which reflect only care provided in previous years. dQMs facilitate rapid generation of critical information, allowing clinicians to use the findings for quality improvement more prospectively, which vastly multiplies the value of measurement.
Also essential are mechanisms of data governance to promote safety, security and accountability through all stages of care and health IT uses. Accurate quality measurement and trust in value-based payment require the assurance of data validity. Data aggregation, validation and measure reporting should be seamless, so clinicians need only record tasks performed in the normal course of care.
NCQA is conducting a pilot that aligns with this goal, with three health information exchanges (HIE) in New York State. The pilot will demonstrate whether HIEs, or other potential data aggregators (or “hubs”), can collect valid, reliable measurement data from clinical practices, hospitals and other providers. If so, this approach can support the draft’s call to harmonize provider data collection and reporting requirements across agencies. If the pilot is successful, we hope to develop a private-sector data hub validation program to assess whether entities can demonstrate an ability to consistently perform this critical task.
Thank you again for the opportunity to comment on the draft plan. Please contact NCQA Director of Federal Affairs, Paul Cotton, at email@example.com or (202) 955-5162 if you have any questions.
Margaret E. O’Kane, President
How Digital Quality Measurement (dQM) Data Move To and From the Point of Care
Digital quality measures (dQM) reduce reporting burden, improve accuracy, provide faster feedback and measure more of what matters by using data already generated in the normal course of patient care.
From the clinician’s perspective, this means simply recording patients’ data in electronic health records (EHR) or other electronic data repositories, as they routinely do. With dQMs, that is the full extent of their responsibility to report quality measurement.
Data aggregators—health information exchanges (HIE), health systems, plans, accountable care organizations and so on—act as “hubs” to gather data from electronic sources. Aggregators have data validated by NCQA to ensure reliability needed for trust in value-based programs, apply the data to measures and report the results to Medicare, Medicaid, health plans and other stakeholders.
Clinicians can further benefit from dQMs’ ability to provide faster feedback. Today, clinicians deliver care one year, report on its quality the next and get feedback down the road. dQMs allow organizations to run periodic gap analyses to rapidly generate critical information, so they can use this knowledge proactively for quality improvement, which vastly multiplies the value of the measurement process.