Data Quality in an Abundance of Electronic Health Information for Electronic Clinical Quality Measures (eCQMs)

Data Quality in an Abundance of Electronic Health Information for Electronic Clinical Quality Measures (eCQMs)


The quality of data is foundational for creating data-driven health care systems, so what is the current landscape for assessing and maximizing the quality of electronic health data for measuring performance? While electronic data poses a great opportunity – an emphasis should be placed on the quality and completeness of that data to use for performance measurement.

Understanding what is working and what isn’t helps move us forward. This was the overarching aim guiding the work of the National Quality Forum’s Electronic Health Record (EHR) Data Quality Technical Expert Panel’s Final Report. The report characterizes the current state of EHR quality, including major findings on what is relevant today, key issues and current best practices to address the issues.

Navigating Through the Challenges

Focusing solely on data quality as it relates to quality measurement (rather than the delivery of care), the following sections describe the high-level obstacles discussed in the report and examples of how the right technology partner platform can help you close these gaps.

  • A limited number of systems have the required eCQM capabilities, citing that there are only 776 health information technology products that are certified on the ability to capture, import, calculate and export quality measure data. Products like KPI Ninja’s Ninja Universe have been externally validated through the National Committee for Quality Assurance through the eCQM Certification program and Drummond Group for ONC Health IT Certification on these capabilities.
  • Measurement depends on data coming from multiple systems, so interoperability is a critical factor in successful reporting, providing examples of measures with data points pulled from multiple EHRs, laboratory information systems and more. Interoperability is powerful stuff – especially when closely aligned to the needs. We’ve been trailblazers in bringing together data sources and formats through our agnostic inbound and outbound capabilities to bring forward the right story more efficiently.
  • Raw data (either from EHR requirements or clinicians’ input practices) do not align with measure specifications, highlighting the need for data transformation. Quality measurement takes a ton of learning, time and let’s be honest, planning to ensure the measures match specifications. By using a partner like KPI Ninja, you can save time and frustration because our standard process includes taking the time to learn your documentation practices to assure the data extracts mimic clinician workflow and data transformation is applied to the right data elements to meet measure definitions.
  • Unstructured data is still used extensively to capture nuanced patient information due to the inability to discretely document clinical information that varies significantly between patients. It’s been estimated that 80% of medical data is unstructured. With numbers like that, it is essential to find technologies that can reduce the amount of work needed to tap into this large volume of data. Luckily, we are thinking ahead by offering our Natural Language Processing capabilities with a rigorous validation process for clients looking to make the most of the data living in their data sets.
  • The post-acute care setting has significant challenges related to financial barriers, ongoing standards and interoperability challenges alongside the quality goals of the IMPACT Act to use EHR data as a data source for quality measurement. Through our work in settings like small primary care practices and rural settings that resemble the technology situation of the post-acute space, we know our clients can see a high return on investment with our interoperable, analytic services that overcome some of these obstacles through strategies like direct database integrations to build measures and data cleansing.

The report lists a few best practices discovered in recent literature to address EHR data quality. Promising strategies like high-quality point of service documentation, thorough data validation and close workflow alignment are just a few of the ways health care organizations are moving toward creating more effective measurement systems for quality measurement.

The report also highlights several frameworks that are used to assess the data quality in EHRs. Taking a closer look into one of the frameworks that was shared, Weiskopf, Bakken, Hripcsak and Wange (2017), we find that many of the data quality constructs and definitions align to capabilities within our “Data Explorer” application – a self-service tool that profiles data at a granular level and enriches it based on standard terminology sets, local trends and end-user configurations.

Organizations, teams and providers recognize that understanding quality measurement requires a lot of resources, which can be costly. Participants using Ninja Universe receive automated, detailed analyses for key data elements, complete with reports, analytics and self-service data management tools to close EHR data quality gaps.

Ninja Universe
Ninja Universe

As value-based care becomes increasingly more important, organizations and teams should be considering their strategy to minimize data quality obstacles to set up for successful reporting and participation. Notably, there is still much work to be done in the quality of electronic health data. And the right technology product can help you get there. KPI Ninja is continuing to advance our product and services in this area to increase the utility of electronic health data to support your goals. To see the product in action, set up a time for a demo today.


Kong H. J. (2019). Managing Unstructured Big Data in Healthcare System. Healthcare informatics research25(1), 1–2.

National Quality Forum. (2020). EHR data quality best practices for increased scientific acceptability: An environmental scan, Final report.

Weiskopf, N. G., Bakken, S., Hripcsak, G., & Weng, C. (2017). A Data Quality Assessment Guideline for Electronic Health Record Data Reuse. EGEMS (Washington, DC)5(1), 14.

Renee Towne

About the Author
Renee Towne
VP of Population Health at KPI Ninja, Inc.
Renee provides operational leadership of quality initiatives at KPI Ninja. Towne has a background in occupational therapy, education and experience in operational excellence across a variety of healthcare domains. Based on prior experience as a clinician that drove outcomes patient by patient, she is leaving a larger footprint by improving health care more comprehensively, population by population.

About KPI Ninja
KPI Ninja is a data analytics company that helps healthcare organizations accelerate their quality, safety, and financial goals with a unique combination of software and service. We are differentiated by our signature mix of technology, performance management consulting and healthcare expertise. We don’t merely offer software solutions but work shoulder to shoulder with clients to help them draw on the power of analytics and continuous improvement methodologies to become more efficient. In harmony with our data-centered ethos, we truly believe that our success is strongly co-related with yours.

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