Effective representation and exchange of clinical knowledge are essential for improving patient care and healthcare outcomes. Therefore, a clear and comprehensive expression of clinical quality logic, including rules, guidelines, and criteria for healthcare evaluation, is crucial for linking clinical knowledge with actionable insights, ensuring healthcare professionals can access accurate information for making informed decisions.
As decision-making within healthcare becomes increasingly dependent on digital tools that use data, presenting clinical knowledge consistently and enabling smooth communication across different systems becomes vital. CQL Clinical Quality Language was developed to represent the clinical logic required to harness insights from healthcare data.
Explore how CQL contributes to knowledge sharing, clinical information expression, and the enhancement of healthcare quality.
How the Clinical Quality Language Works
Clinical Quality Language, or CQL, is an authoring language standard designed by the Health Level 7 standard developing organization to express electronic clinical quality logic in both computable and human-readable formats to act as a bridge between raw healthcare data and meaningful insights, enabling health professionals to make informed decisions.
CQL supports the Clinical Decision Support (CDS) and Clinical Quality Measures (CQM) domains by providing a standardized approach to representing the clinical quality logic in any given artifact by expressing it in high-level CQL format and machine-readable canonical representation called the Expression Logical Model (ELM). This standardization aligns with initiatives like ISiK Compliance, which emphasize the importance of unified data exchange protocols in healthcare systems.
Expression Logical Model (ELM)
The Expression Logical Model (ELM) is the essential part of the Clinical Quality Language specification aimed at translating high-level CQL logic into machine-executable code as an Abstract Syntax Tree (AST), prioritizing core logic and using formats like XML and JSON for communication. The rendering process includes steps like figuring out the order of operations, understanding types, converting data types, etc.
The resulting ELM representation captures the underlying logical structure and reasoning embedded within CQL expressions, making it adaptable for execution in various computing environments.
Architecture of CQL
Authors use CQL to create libraries containing precise logic and structure that not only developers but also domain experts can easily understand. These libraries are then converted into machine-friendly ELM XML documents designed for the distribution of CQL. Implementation platforms can execute the ELM directly or translate it into their preferred programming language for practical application.
CQL provides authors with a user-friendly syntax, supporting language processing applications with a logical representation, and establishing a seamless translation mechanism between the two, thereby facilitating sharing and collaboration in healthcare quality measurement and clinical decision support.
The Role of CQL in Healthcare
While both Clinical Decision Support and Clinical Quality Measurement aim to enhance healthcare quality, standards used for representing their artifacts lack compatibility, resulting in limited expressiveness. Clinical Quality Language (CQL) was developed to harmonize these differences and ensure a unified understanding of clinical knowledge across various healthcare domains.
CQL for Clinical Decision Support
Clinical decision support (CDS) systems are designed to support clinicians by providing essential clinical knowledge via medication alerts, best practice notifications, and documentation templates, ensuring that decisions rely on the latest data available and reducing the risk of human error. So what clinical quality language examples are there?
One of the examples of clinical quality language usage is when a patient has specific observations with particular values and has not undergone a specific examination, CQL can initiate a referral for diagnostic testing by evaluating predefined clinical logic against the patient’s data, indicating the need for further investigation. This functionality streamlines decision-making, ensuring patients receive timely interventions based on established clinical protocols, ultimately enhancing healthcare outcomes.
The precise expression of clinical quality logic allows the articulation of clinical knowledge and furnishes healthcare professionals with timely guidance during patient care. CDS systems draw upon clinical expertise to highlight possible issues or opportunities for intervention, ensuring that healthcare professionals provide evidence-based recommendations to patients. If clinical knowledge is not adequately formulated, there is a risk that a CDS system may fail to offer valuable guidance, potentially resulting in lowered healthcare quality and poor patient outcomes.
CQL for Clinical Quality Measures
Electronic Clinical Quality Measures (eCQMs) are standardized metrics for assessing healthcare quality that rely on data from Electronic Health Records (EHR) and health IT systems. eCQMs are used to analyze data for evaluation of such aspects of patients’ care as their engagement, safety level, healthcare process coordination, and how resources are used, among other things.
Leveraging CQL on large datasets allows for querying specific patient indicators and calculating conditional statistics, which is essential for population health management and decision-making at governmental levels. This functionality enables timely responses to disease outbreaks and informs resource allocation, such as vaccine distribution.
eCQMs rely on clinical quality logic to define measurement criteria and ensure that assessments are standardized, reliable, and meaningful across diverse settings and populations. Without a clear and detailed way of expressing clinical quality logic, it can be challenging for eCQMs to measure and evaluate healthcare performance accurately, which can hinder the process of improving healthcare quality.
Previously, generating population health-related statistics was burdensome for healthcare providers. However, with the advent of data-driven approaches, Clinical Decision Support (CDS) systems, and leveraging CQL, these tasks can be automated, relieving clinicians of reporting burdens and facilitating informed decision-making.
Why Leveraging CQL?
The recent changes in The Office of the National Coordinator for Health Information Technology (ONC) (HTI-1) Final Rule Certification Program promote the adoption of the Decision Support Interventions (DSI) certification criterion, aiming to ensure that decision support systems are built upon current scientific research and clinical guidelines. This initiative is expected to empower healthcare professionals to make well-informed decisions supported by robust evidence. Moreover, ONC mandates the implementation of Fast Healthcare Interoperability Resources (FHIR) to enhance interoperability in healthcare systems.
Also, the Centers for Medicare & Medicaid Services (CMS) advocates for adopting electronic clinical quality measures (eCQMs) within the Medicare Promoting Interoperability Program to guarantee that healthcare services exhibit effectiveness and safety while prioritizing equal, timeless, and patient-centered care.
We will likely see CQL and eCQMs-related requirements for decision support systems regulations in the future since well-structured and comprehensive data is established by integrating FHIR and Clinical Quality Language, which allow for automating rule-based clinical decision support systems, treatment protocols, and guidelines.
CQL is the standard that holds the potential to streamline healthcare processes. If you want to comply with regulations directed at improving clinical decision support systems, integrate FHIR for interoperability, and leverage CQL to harness clinical knowledge effectively, contact us for a strategy for adopting these transformative technologies and practices.
FAQ:
Is CQL and Cassandra the same?
No, Cassandra Query Language (CQL) is a database communication standard, while Clinical Quality Language (CQL) is a standard language for representing clinical quality logic in healthcare.
What is clinical quality language in healthcare?
CQL stands for Clinical Quality Language, a standardized language for expressing clinical quality logic in human and machine-readable formats.
What is the purpose of clinical quality language?
CQL was designed to support healthcare professionals by bridging raw healthcare data to meaningful insights.
