Understanding Clinical Quality Language (CQL)
How Healthcare Quality Measures Work
The Center for Medicare and Medicaid Services (CMS) employs Clinical Quality Measures (CQMs) to measure the quality of healthcare services provided by eligible professionals, eligible hospitals, and critical access hospitals. Healthcare providers are required to report CQMs electronically (eCQM) by using the data extracted from health information systems like EHRs (Electronic Health Records). The CMS uses eCQMs for a wide variety of reporting and value-based purchasing programs, including the assessment of treatment outcomes by healthcare providers and organizations.
The lifecycle of eCQMs progresses through the following phases:
Conceptualization of Measures: Measure concepts, measure gaps and the evidence for measure based concepts that are required for the logic are evaluated in this step to develop the reporting protocols. The basic elements of a measure comprise the numerator and denominator values based on data which determine the efficacy of medical procedures.
Specification of Measures: Electronic measures are defined and developed by a measure developer, measure stewards, and tool owners in order to increase the consistency, reliability and effectiveness of reporting.
Measure Testing: The feasibility, usability, and scientific acceptability of a measure are tested by health IT vendors, hospitals, and measure developers.
Measure Implementation: HIT vendors and care providers implement measures for a given quality measurement program.
Measure Maintenance and Use: Stakeholder feedback on the use of measures and the implementation process is received by the CMS and shared with measure developers for the improvement of future versions.
Challenges of the Current Metric Collection and Reporting Process
eCQMs are created using a combination of healthcare quality measure formats (HQMF) and quality data models(QDMs). Since none of these formats provide computable logic specifications, a measure developer is required to manually translate and implement specifications based on the development environment. This vastly limits the easy exchange of data across healthcare organizations.
Some of the other challenges that both providers and payers face with the existing metric collection and reporting process are as follows:-
- The lack of a unified expression language to report metrics accurately by reducing the dependency on various data sources
- The increased risk of the variability of measures and data
- The loss of revenue due to inaccuracies in reporting caused by the workload on measure developers
- Increase in implementation costs of measure development
eCQMs are currently created and structured by individual healthcare organizations based on CMS requirement mandates. This leaves healthcare providers with a constant struggle to exchange clinical information across disparate healthcare information systems without increased technology costs.
The CQL Solution
CQL (Clinical Quality Language) was adopted by the CMS to resolve the health concepts fragmentation issue when representing CQM measures. It is an effort from the CMS to harmonize standards used for eCQMS and CDS (Clinical decision support). A CQL logic can be directly used by a CDS engine to share common definitions with CQM measures. Before the adoption of CQL, quality measures and decision support logic were implemented independently. This resulted in inconsistent measure results and CDS logic definition. By unifying the logic of quality measures, CDS logic and clinical pathways, CQL vastly reduces the possible errors of interpretation.
CQL provides the following solutions to the challenges that care providers deal with when simplifying the representation and reusability of the logic used in quality measures and support.
Simplification of Defined Value Sets: CQL saves measure developers the ordeal of interpreting quality measures and CDS logic to suit their local reporting needs. It does this by providing them with well-defined data requirements, medical terminology and value sets that can be standardized to enable sharing over disparate health information systems.
Reduction of Implementation Costs: CQL eliminates the manual translation of measures and CDS information which dramatically reduces the time required by measure developers in the development process.
Reduce Unwanted Variability in Measures: CQl measures and CDS data eliminate the possibility of varied interpretations by maintaining uniformity in the logic of the measure.
Reduction of Clinical Burden: Since CQl is human-readable, it means that clinicians can specify computable logic without having to write requirements and waiting for results to grade. Clinicians find CQL so simple that they can even edit and author measures logic themselves.
Adopting CQL as the Industry Standard
At Nalashaa Health Care Solutions, we are well versed with the measure specification, implementation and testing process using the Clinical Quality Language. We work closely with care providers and payers to collaborate and systematize the design of new measures, enabling them to leverage the benefits that CQL brings to the continuum of care.
To know more about the Clinical Quality Language, reach out to us at info@nalashaa.com.
Amit Manral