The Quest for Harmonization of Healthcare Quality Reporting Standards
The year 2018 was busy and fruitful for the CMS and ONC, the two federal agencies that are responsible for the improvement of healthcare standards of all American citizens. From the release of the Trusted Exchange Framework and Common Agreement (TEFCA) to the announcement of the Data Element Library (DEL), both these vital cogs of the United States Department of Health & Human Services (HHS) have been doing a laudable job of ushering the US Healthcare industry towards better care outcomes and patient empowerment.
The Clinical Quality Language (CQL) is the latest initiative of the CMS and is set to become the new mandated standard for eligible hospitals and providers for electronic Clinical Quality Measures (eCQM) reporting. CQL is an expression language that allows the authoring of both eCQM and Clinical Decision Support (CDS). The fact that it can achieve this harmonization of these two standards, opens up new possibilities in the coordination of the vision, workflows, and technologies used by care providers.
Why is there a need for CDS and eCQM Harmonization?
CDS and eCQMs are two sides of the same coin. While eCQMs measure the quality of care, CDS logic leverages the effectiveness of the actions that make up this quality metric to improve care outcomes.
For example, patients suffering from ischemic stroke are often prescribed anticoagulants. From a quality measure perspective, the percentage of patients who are given anticoagulants to successfully reduce the risk of cardiac arrest and muscular paralysis are recorded. Based on the effectiveness of this data, CDS provides caregivers with actionable insights in real-time, helping them choose the best treatment plan for patients who are diagnosed with ischemic strokes.
Such an extent of co-relation is possible because CDS and eCQM specifications share many common standards. They both 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 costs and efforts required in implementing them with the possibility of automation.
The harmonization of CDS and eCQM will ensure the ease of their implementation. It will also help providers modularize existing standards by sharing the data models and efforts required in the creation of these quality measures and CDS logic.
Overcoming an Age-Old Industry Challenge
Despite making perfect sense, the harmonization of CDS and eCQMs has been a challenge due to the following reasons:
• Current EHR systems often use disparate tools for CDS and eCQM implementations
• The use of these different standards for the representation of CDS and eCQM makes reuse of business logic extremely difficult
• Measure developers are required to manually translate quality specifications to facilitate CDS logic
• The manual translation process creates variability in measures interpretation and implementation
• The resulting variability leads to care providers incurring further costs in establishing accuracy standards for reporting.
How CQL Delivers an Effective Fix
The current 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 QDM provides, to finalize the HQMF. QDM has two parts – the data model and logic.
CQL replaces the QDM logic, into groups 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. The following are some of the benefits of CQL that promote the 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
● 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 the 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 measure criteria to create CAT 1 and CAT 3 files.
This solution can be effectively extended to deliver CDS alerts. This technical approach to the definition of CDS logic enables providers to get better quality scores and make the best treatment decisions.
Conclusion
Providers can now reduce the usage of resources for measures and CDS implementation by up to 90% with the release of the CQL mandate by the CMS. Some of the key services they need to complete the transition into a CQL-based configuration includes the implementation of off-the-shelf and easily shareable CQL measures, CDS logic and app customized development services.
Nalashaa Healthcare Solutions helps providers make this transition by implementing CQL compatible solutions to streamline healthcare quality reporting and clinical decision support.
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