Understanding FHIR-CQL

 It was inevitable that dQM—digital quality measures—would collide with the interoperability world. That’s not only a good thing; it makes perfect sense. While dQMs provide machine-readable measure definitions and corresponding data specifications, interoperability standards define the structure, transport and security of clinical data used for various use cases, including quality measurement. In other words, dQM standards apply “business rules” to clinical data. Interoperability standards enable uniform access and retrieval of clinical data from disparate sources via APIs (real-time interfaces) and both contain data definitions. 

As FHIR was being developed (and would quickly become the de facto standard for interoperability), the primary standard for dQMs had two components: 

1. Quality Data Model—QDM—a standard and machine-readable way of defining data elements and structure for data to be consumed and processed by CQL. 

2. Clinical Quality Language—CQL—a standard and authoring language that is both a human- and machine-readable way of declaring all the quality rules for a given measure. 

Because both FHIR and QDM-CQL already share the same underlying (modern, widely adopted) technologies, it only makes sense to merge data definitions. And because both were developed under the HL7 umbrella, a smooth transition from QDM-CQL to FHIR-CQL is ensured. 

But before we see full commitment and transition to FHIR-CQL, we need a test period—and that’s exactly what NCQA is doing. A set of measures for trial use will be released in late summer 2020 and will be available in the MY 2020–2021 Measures Bundle. 

If the trials go as expected, there will be a transition from QDM-CQL to FHIR-CQL in relatively short order. In addition, we will likely see additional measures (traditional measures, ECDS) implemented in FHIR-CQL. The ultimate goal is to have all measures in one dQM standard (FHIR-CQL being the obvious candidate), which will not only simplify measure definitions and software development, but will also significantly further automation and streamlining of data collection and measure reporting.  

 While it might be premature to claim that FHIR-CQL can be implemented for all measures, we can say that it will blaze a trail—especially given that clinical data is becoming more structured, standardized and universally
accessible, thanks to interoperability initiatives already underway. 

Additional Resources 

Episode 5 of the Future of HEDIS webinar series provides another great overview of FHIR-CQL (if you’re in a hurry, forward to 11:45). 

Share your thoughts in the community forum: 

· What else would you like to know/understand about dQM and FHIR-CQL? 

· How soon would you like to see the transition to an all-dQM future? 

· What meaningful milestones do you envision on the way to an all-dQM future? 

· What obstacles do you anticipate in transitioning to dQMs? 

· What tools and support would you like to see to aid the transition to dQMs?