The Center for Medicare and Medicaid Services (CMS) and the Office of the National Coordinator (ONC) are two federal agencies that are tasked with improving the healthcare standards of US citizens. They have been doing a great job of steering the healthcare industry towards better care outcomes and patient empowerment. They have been doing this by introducing important regulatory frameworks that promote the use of cutting edge technologies and communication between payers, providers, and patients.

Clinical Quality Language (CQL) is one such initiative to come from the CMS. It is set to be the new standard for eligible hospitals and providers for electronic quality measures (eCQM) reporting by 2020. It was released by the CMS to facilitate the sharing and reusability of the core components of healthcare measures and clinical decision logic. This could lead to significant cost savings for providers in the measures submission process.

CQL opens up the possibility of cost reduction for hospitals with the streamlining of the measures submission process. Traditionally the efforts needed by measure developers to was both intense and exhaustive. This is because they needed to rewrite QDM logic from scratch to make it readable by Clinical Decision Support CDS systems.

Thanks to the advent of CQL, they can now leverage multiple ‘reusable’ components that help them create data logic for both measures submission and CDS use. This helps care providers save significant amounts of time and costs.

The Correlation Between eCQMs and CDS

Consider the following clinical scenario. Patients suffering from Ischemic strokes are given anticoagulants as a part of the standard treatment procedure. Hospitals record the number of patients suffering from Ischemic strokes who were given anticoagulants and their effectiveness in preventing cardiac arrest and muscle paralysis.

The hospital now has data to present their performance in the treatment of stroke patients. It is used to create eCQMs, the reports that every hospital needs to submit to the CMS to receive incentives. The same data can be fed to a hospital’s Clinical Decision Support (CDS) system for doctors to refer to and choose the best treatment plan for future patients.

This correlation is possible only because of the common standards that both eCQMS and CDS data models use. They both include the metadata population structure, the logic computation model, and the Quality Data Model (QDM). The presence of many reusable elements in both these specifications helps care providers save costs by reducing the effort needed in implementing them.

The CMS’s focus on integrating Clinical Decision Support (CDS) and eCQMs is aimed at improving the quality of care by coordinating the vision, workflows, and technologies. There are many industry challenges that this approach solves for providers. The following are the key advantages that this strategy unlocks for the US healthcare industry:

• The unification of CDS and eCQM will help improve care quality by providing feedback to relevant stakeholders.
• Understanding healthcare system transformation becomes easier.
• eCQM results support the iterative implementation of CDS initiatives.

 While the idea behind this vision is promising, there are significant challenges to implementing it. Currently, EHR systems use disparate tools for CDS and eCQM implementations. The use of these different standards for the representation of CDS and eCQM makes reuse of logic between their rules extremely difficult. Measure developers are required to manually translate quality specifications to facilitate CDS logic.

 This need for manual translation creates variability in measures interpretation and implementation. This makes reusing and sharing of logic between eCQMs and CDS difficult. This lack of harmonization between these two important data components of the healthcare quality improvement initiative has created the need for a better solution to integrate eCQM and CDS logic.

Exploring the need for CDS and eCQM Harmonization

 CDS and eCQM specifications share many common standards. They include the metadata population structure, the logic computation model, and the quality data model (QDM). There are many reusable elements in both the specifications that can help reduce the cost and effort required in creating them, with the possibility of automating the creation of these specifications.

 With the harmonization of eCQM and CDS, the implementation of quality measures becomes easier than it was earlier. It will also help providers modularize existing standards by sharing the data models and the efforts required in the creation of these quality measures and CDS logic.

The CQL Solution

 Currently, the industry standard uses Health Quality Measure Format (HQMF) and the quality data model (QDM) to express measure specifications. HQMF comprises the basic electronic specifications for the measure while the QDM provides information to finalize the HQMF. It is made up of two parts – the data model and logic.

 CQL replaces the QDM logic in a measure, into elements that can logically be referenced from different specifications. This makes it well-suited to express CDS logic without requiring the manual translation of the information included in the measures. Here are some of the benefits of CQL that promote harmonization of eCQM with CDS.

• Provides customized QDM logic expressions for both CDS and eCQM
• Data model agnostic
• Provides both author-friendly and machine-friendly syntax
• Automatic transformation to machine-friendly syntax
• No need for manual mapping (unlike QDM/HQMF)
• Point-to-point sharing of executable clinical knowledge
• Can be used with multiple information data models like QDM and FHIR

 The Technical Approach to Implementing CQL

A CQL engine interprets CQL grammar through the lexical analysis and parsing of components to identify the respective population criteria based on the measure. This is integrated with different FHIR queries to import information from EHR systems. The data from the EHR system is parsed based on the defined criteria of the measure to create CAT 1 and CAT 3 files which are submitted by providers as a part of their quality reporting duties.

 This solution can be effectively extended to meet CDS requirements as well to deliver alerts to providers. This technical approach to the definition of CDS logic enables providers to perform better quality reporting and make the best possible treatment decisions.

Conclusion

Providers can now reduce the usage of resources for measures and clinical decision support implementation by up to 90% with the release of the CQL mandate by the CMS. Some of the key services they need to fulfill the complete transition into a CQL-based configuration include the implementation of off-the-shelf and easily shareable CQL measures, CDS logic and app customized development services.