The Standard

Federal health agencies have entered an era where data interoperability-enabled solutions play a critical role. Government leaders can harness the innovative and proven capabilities referenced in this article to deliver on their essential missions.

Background

In 2020, two major events laid the foundation for this era of interoperability.

Pandemic Response: The first was the coronavirus pandemic, which led to unprecedented needs for health data in support of agency missions. Since its start, decision-makers have required more access to and insights from these data (e.g., clinical records, administrative claims, patient experience) than ever before.

Interoperability Rules: The second was the finalization of the ONC and CMS-led 21^st Century Cures Act interoperability rules. These mandates substantially expanded agencies’ ability to leverage health data solutions (e.g., algorithms, applications, and automation) at scale.

Since the fall of 2017, electronic Clinical Quality Measures (eCQMs) logic has been written using Clinical Quality Language (CQL). With the introduction of CQL, the eCQMs included in the CMS federal programs had to be translated from Quality Data Model (QDM)-based expression logic to CQL.

Though CQL provides more flexibility in expressing measure criteria within the logic that allows measure developers to include greater specificity and precision within the measure requirements, this did not result in a direct translation process, as new attributes and timings were made available. What this means is that clinical workflow and input are perhaps more important because CQL is able to convey these nuances, whereas the QDM-based logic expressions were limited. CQL can also be used to develop Clinical Decision Support (CDS) rules and can reuse logic from measures in those CDS rules.

When composing measures using the CQL syntax, the QDM is still used in conjunction with CQL as the means to characterize required data elements within the measure logic. This conceptual model defines what type of clinical data is needed to satisfy the measure requirements. Therefore, the QDM is still very much a part of electronically specified measures, which is incorporated into the logic definitions written using CQL.

Health Level Seven (HL7^®) drives the standards upon which electronic Clinical Quality Measures (eCQMs) sit, so participation in these venues is crucial. The HL7 community already has a lot of technical input but lacks input from the clinical side – either from clinicians or clinical informaticists. There is a gap between the clinical intent of quality measures and the technical standard, but clinical informatics experts can help bridge this gap by contributing to the conversation and development of the HL7 standards and resources.

Measure development, as we know it, is changing. Quality improvement is important to keeping healthcare costs affordable and patients safe; however, it should not come as a huge burden to clinicians. Goals of electronic Clinical Quality Measures (eCQMs) include reducing the burden of manual abstraction and reporting for provider organizations, as well as fostering the goal of access to real-time data for bedside quality improvement and clinical decision support. We want to hit the “sweet spot” as depicted in this diagram:

WHY STANDARDS?