Why Digital Quality

For more than 30 years, the National Committee for Quality Assurance has led the health care quality movement. As the nation’s leading measure developer, NCQA is the steward of the Healthcare Effectiveness Data and Information Set (HEDIS®), one of health care’s most widely used performance improvement tools.

But traditional quality measurement has limitations. HEDIS is incredibly valuable for quality improvement and benchmarking at the health plan level. In the digital age, with data more readily available, there’s the opportunity to make measurement more accessible and actionable closer to the point of care in a way that can help better manage populations, support more equitable care and drive outcomes.

What is Digital Quality?

Digital quality means moving to standards-based technology that makes quality operations simpler and more efficient. More importantly, it is about more relevant measures and the increased use of clinical data to enable better measurement, better care and new payment models.

Digital quality uses standardized, digital data from one or more sources of health information that is captured and exchanged via interoperable systems and applies quality measure specifications that are standards-based. Digital quality measurement leverages code packages and is computed in an integrated environment without additional effort. The solution enables:

Data queries from standards-based application programming interfaces (such as FHIR® APIs).
Measure score calculation.
Generation of outputs necessary for quality reporting.

It is part of the learning health system (LHS) to improve patient care and experiences by ensuring patient and provider access to necessary information in a timely manner (rapid-cycle feedback).

Learn more about digital quality here.

Why Digital Quality?

Emerging standards and regulations are enabling a digital transformation to enable quality to be better aligned with care delivery and a learning health system. This will lead to reduced burden and costs, better alignment, more relevant measures and ultimately, better care & outcomes. NCQA has invested heavily in building a digital quality ecosystem to create more efficient data collection and reporting, and better accountability at all levels of the healthcare system.

Achieving a Quality-Enabled Learning Health System

To help solve for some of the challenges in the quality ecosystem, the industry is driving standardization. These standards and emerging tools create the foundation for a continuous learning health system where clinical guidelines, measurement and data collection can help improve care and enhance measure development faster and earlier.

A learning health system continuously leverages data to generate knowledge that can be used to improve care. In health care quality context, a learning health system standards-based quality content is developed in an actionable way based on that knowledge. Those actions taken through clinical encounters can then be captured and turned into data. That data can then be used for measurement and analytics and turned back into knowledge, which feeds back into the cycle to support better care and improved patient outcomes.

As CMS notes in its Digital Quality Measurement Strategic Roadmap, “In a learning health system, standardized and interoperable digital data from a single point of collection can support multiple use cases, including quality measurement, quality improvement efforts, clinical decision support, research, and public health. Data used for quality measurement, as well as these other use cases, should be a seamless outgrowth of data generation from routine workflows.”

Elements of a Learning Health System

Key question	Historical limitations	How this is improved through digital quality?
Are standards and guidelines consistent and actionable (i.e. – Can the standards and guidelines for care be interpreted and applied the same way across the healthcare system and are they in a usable format for delivering care?)	Content was often fragmented, narrative-based (human interpretable but not fully-specified or computer-interpretable) and leave room for interpretation, leaving the guidance for care often disconnected from quality.	NCQA is building practice guidance and standards into digital quality content. Configurable measures become usable for quality improvement and population health purposes.
Is data collection and transfer agreed and trusted? Do the definitions of the data mean the same thing and can you trust the accuracy and validity of the data?	Data collection and transfer has long been an industry challenge, with variation in how data are captured, whether the data are captured in a way that is shareable and whether the data are even accurate, leading to variation and a need for data standardization.	NCQA is leveraging common data standards and building measures, tools and programs that help standardize data expectations for quality, validate data used for reporting and performance and build trust in data.
Is measurement clinically relevant and supportive of value-based care?	HEDIS and other existing measurement systems have worked, but they have been limited by data (largely administrative historically) and typically are retrospective.	By building digital, configurable measures leveraging a broad range of standardized data sources, NCQA can build and deliver better measures that are more clinically relevant and better support value-based incentive programs across contexts and levels of healthcare, at lower cost and easier distribution.
Is there common understanding of implementation throughout the industry to promote broad adoption and confidence?	Different organizations work on different pieces of quality in ways that are somewhat disconnected. This results in manual, duplicative and wasted effort to figure out the guidelines and how to build them into applications, as well as how to manage and validated data. Those inefficiencies likely amount to tens of billions of dollars a year for the healthcare industry.	NCQA has always been an organization focused on convening stakeholders and building consensus. It will continue to build resources and stakeholder engagement to support the standardization and implementation of digital quality.

Why Now?

The quality industry has recognized the need for improved efficiency for a while, but there are three things coming together to make this the right time for digital quality adoption: Market signals about where improvement in quality needs to happen, the maturity of standards in the industry making this technically possible, and the evolving payment arrangements shifting from fee-for-service to value-based, where more of the industry is taking on risk.

Market Signals

The market is asking for reduced measure burden, more usable measures and measures that better support value-based care. Specifically, the industry is asking for measures and quality content that:

Lowers cost, burden and variability: Can measure content be developed and distributed in a way to reduce interpretation, development, and maintenance needed today?
Supports the learning health system use cases: Can measures be configurable and used in different workstreams for different use cases, including quality improvement, population management, and analytics?
Better supports value-based care: How can quality measures move beyond signals or gates to meet evolving VBC needs? The industry needs connected (data) and consistent (methodology), built around priority populations and conditions, to be relevant and actionable across contexts and accountability models.

Maturity of Standards

The industry has taken steps towards data standards for improved interoperability as regulatory forces propel momentum forward. This includes both regulatory momentum and industry-driven standardization.

The federal government has also embraced FHIR® and CQL to promote trusted data exchange. In 2020, ONC and CMS issued rules requiring EHR technologies and health plans to implement FHIR®-based application programming interfaces.
CMS has prioritized digital quality measures in an effort to improve the quality and usefulness of clinical data. CMS has set a goal of transitioning to all digital measures by 2030 and its “Universal Foundation” aims to align quality measures across CMS quality programs.
Industry trailblazers and standards organizations are carving out paths for data standards to support interoperability and data exchange. Examples include HL7® FHIR® interoperability standards, United States Core Data for Interoperability (USCDI) standards for clinical data exchange, the CARIN Alliance Blue Button framework, which developed standards to support claims data interoperability, and the Gravity Project to advance SDOH and health equity interoperability.

Payment Arrangements

The financial shift from fee-for-service to value-base care continues, creating greater need for quality initiatives, accountability and measurement at all levels of healthcare, as well as measures that can better support priorities in value-based care.

Benefits

Curious about the benefits of making the transition to digital quality?

Benefits to All Organization Types

Standardized data eliminate the need for separate supplemental data files in Excel, CSV and CCD format
Measures are pre-validated
Measures execute “out of the box,” and don’t require repeatedly coding and re-coding
Increased trust in data quality
Expanded use cases and better support of value-based care without needing to store full patient data
Can see measures, description and metadata with CQL and measure packages
Run measures on demand individually, bundled, as a subset or ad hoc
Apply configurations per measure/bundle or per population; see outcomes based on performance management needs
APIs used to exchange the data on demand and efficiently
Software identifies errors or nonconforming data in real time
Easily integrate outcomes into dashboard, EHR, clinical views and other downstream products

Benefits to Health Plans

Clinicians and teams can focus on their work and managing care instead of building and managing measure processing
Improved efficiency and accuracy of clinical data and outcomes
No need to send Excel files to providers to fill in supplemental data
Decreased need for “chart chase” and medical chart review
Easier to integrate into SMART on FHIR apps and other digital workflows
Easier to run multiple years and populations simultaneously to support population stratification
No need to interpret specifications year over year; measures are delivered exactly as they should be built

Benefits to Care Delivery Organizations

Insight and outcomes based on more standardized clinical data.
Supports proactive not reactive outcomes. Clinical data is more real time, and allows for more frequent updates to show met and not met status
Able to apply predictive logic to indicate a gap before it becomes a gap
Enhanced configurability to meet more use cases than Allowable Adjustments. Can apply more population level stratifications and configurations that keep the measure integrity and allow for more in-depth population stratification and analysis
Allows for more data sources and accessing clinical data. Additional data can increase met patients. Less time will be spent on chart chasing with standardized data. Increased access to lab
Easier to integrate into SMART on FHIR^® Apps and other digital workflows
No longer have to interpret specifications each year, the measures are delivered exactly as they should be built.

Benefit to Tech Solutions and Services

Technology solutions and services that are supporting digital quality measurement will see increases in efficiency of resources, time and processing. Utilizing standardized data decreases the burden of data mapping.

Measures can be turned around to their customers sooner. Due to receiving already built and certified measures vendors don’t need to spend the time coding their content. Customers can use that content early in the program year of their gap in care work.
Vendors don’t need as many engineering and internal resources to code, re-code, update and validate the same measures YoY. Decreased engineering capacity required and those teams can support other work.
No longer have to interpret specifications each year, the measures are delivered exactly as they should be built.
The certification process can move quicker and won’t require all the additional teams. i.e. Data Integration, QA, engineering, etc.
Cloud execution can decrease processing on technical stack
Reduces re-development of measures YoY