Guest blogger Dr. John Halamka is the CIO and VP of Information Systems at Beth Israel Deaconess Medical Center and International Healthcare Innovation Professor at Harvard Medical School.
I served the George W. Bush administration for four years running the Health Information Technology Standards Panel and the Barack Obama administration for six years co-chairing the Health Information Technology Standards Committee. In both venues, the development of flexible, agile, and easy to use quality measure tools was a major focus. We worked on various Clinical Document Architecture prototypes such as the Health Quality Measures Format (HQMF) and the Quality Data Model (QDM). The ultimate goal was to reduce the burden of quality reporting by creating a simple way to define numerators/denominators for submission to quality measurement databases. We envisioned a day when instead of pushing data, queries would enable the real time pulling of quality information directly from EHRs.
Today, we’re on the verge of such tools.
Clinical Quality Language (CQL) brings together the worlds of automated quality measurement, and clinical decision support with one, industry standard language. The Centers for Medicare and Medicaid (CMS) will use CQL as its standard for 2019 electronic clinical quality measures (eCQMs) for eligible hospitals and providers.
What exactly is CQL?
CQL is the new standard in healthcare, endorsed by Health Level 7 International, CMS, the Office of the National Coordinator for Health Information Technology, and the Centers for Disease Control and Prevention. CQL is an expression language for specifying clinical logic that can readily be computable. CQL understands healthcare concepts such as patients and populations, temporal operations, and value sets.
Why is CQL an improvement on HQMF/QDM approaches?
Prior to CQL, HQMF was used in combination with QDM to describe automated quality measures. HQMF and QDM don’t provide computable logic specifications and a measure developer was required to translate the specification into a different language and computing environment, creating a custom implementation. With this manual translation, you’re locked into both the technology and data architecture you’re using because the measure is unique to your organization. Manual translation can also cause variability in the interpretation and implementation of measures, making those measures incomparable between healthcare institutions. Variability in measures creates wasted cost, time and resources, and in the extreme, can make measures practically meaningless.
CQL is intended to resolve the fragmentation by creating a single universal language friendly enough for humans to communicate with, yet precise enough for machines to interpret. CQL provides all in one mechanism to represent quality measures, CDS, and other computable healthcare knowledge such as an ePathway.
How will CQL work with existing EHRs and databases?
Innovators such as Apervita provide CQL tools and implementation services for healthcare enterprises for quality measures, decision support, and clinical pathways. Apervita provides an integrated platform to author, import, test, execute, and share CQL on a single, open platform and at large scale, turning data into insight.
Restoring joy to clinical practice
I’m a great fan of emerging apps and cloud hosted services that layer on top of existing EHRs to enhance functionality and usability. Our existing EHR vendors are doing good work, but must focus first on regulatory imperatives. Innovators can focus on high value improvements that may not be on an EHR vendors’ short-term roadmap. I believe that 2019 will be the year of the EHR connected app and cloud service, bringing joy back to the practice of medicine and reducing burdens on IT departments nationwide. CQL and companies that support it will finally realize the dream we had in the Bush and Obama administrations: quality measurement without pain.