Arpan Saxena is the Head of Product at basys.ai (based out of Harvard University), a leading healthcare AI solutions company.
While AI dominates headlines for its breakthroughs in creative fields and business automation, its potential to reshape healthcare is still emerging. Clinical Quality Language (CQL), for example, offers a potential area for AI transformation.
CQL is a standardized language that can express clinical knowledge and logic in a machine-readable format. Historically, CQL has been used by health IT systems to encode clinical guidelines and quality measures, ensuring consistent interpretation across various healthcare platforms. Organizations like the Centers for Medicare & Medicaid Services (CMS) and other clinical data standards groups have employed CQL to automate quality measure reporting and clinical decision support.
The integration of generative AI with CQL can allow healthcare providers to leverage AI without needing extensive technical training. Typically, writing CQL requires specialized skills. However, generative AI offers the potential for clinicians to articulate their reasoning in natural language that AI translates into CQL code.
In this article, I’ll look at four areas where generative AI can transform CQL as well as what it will take to successfully achieve these outcomes.
Improving Interoperability
Interoperability remains a significant challenge in healthcare. Despite frameworks like Fast Healthcare Interoperability Resources (FHIR), AI insights often struggle to bridge gaps between different systems. Integrating CQL can help ensure that AI-generated recommendations can be understood and acted upon across electronic health records (EHRs), clinics, payers and hospitals.
Organizations are already testing the waters by combining generative AI and interoperable frameworks to enhance care coordination. This trend underscores the importance of creating AI-generated insights that are more than just isolated outputs—they are part of a continuous, collaborative healthcare ecosystem. These insights, when encoded with CQL, become actionable across multiple platforms, breaking down barriers that have hindered effective data sharing.
Reducing Administrative Burden
Administrative tasks are a significant source of clinician burnout, with many providers spending more time on paperwork than on patient care. Generative AI, combined with CQL, has the potential to transform how healthcare organizations approach complex processes, such as prior authorizations, medical billing and claims review. By automating these tasks and encoding decisions in CQL, organizations can reduce the time spent on manual paperwork and redirect that time toward patient-facing activities.
However, it’s important to note that the implementation of these technologies isn’t without challenges. Healthcare organizations need to ensure that AI-generated CQL aligns with both local and national regulations, and rigorous validation processes must be maintained to guarantee accuracy and reliability. This balance between automation and oversight is crucial to successfully integrating AI into everyday operations.
Supporting Value-Based Care
Value-based care has been an aspirational goal for healthcare systems, focusing on patient outcomes rather than procedural volume. Generative AI and CQL can enable real-time, data-driven care models that link care delivery directly to outcome-based reimbursement models. This integration allows healthcare providers to adopt personalized, outcome-focused care plans and ensures these plans are encoded in CQL to support reimbursement and reporting requirements seamlessly.
Some healthcare systems, for example, have started using AI to identify high-risk patients early and suggest preventive measures. When combined with CQL, these suggestions can be encoded into clinical workflows, ensuring they are actionable and interoperable across different systems. This not only promotes a proactive approach to patient care but also fosters stronger collaboration between providers and payers, enhancing trust and transparency.
Embedding Fairness And Transparency In AI-Driven Healthcare
One of the most pressing concerns in AI adoption is bias, particularly in healthcare, where disparities can have severe consequences. Integrating CQL with generative AI provides a mechanism for creating transparent audit trails. These trails enable healthcare providers to verify and correct potential biases in AI-generated recommendations, ensuring inclusivity and fairness in patient care.
However, to truly embed fairness, healthcare organizations must adopt robust data governance practices and bias mitigation strategies. This requires continuous monitoring, diverse data representation and an industry-wide commitment to ethical AI deployment. Companies that prioritize these practices will set the standard for responsible AI use in healthcare, ensuring that advancements do not perpetuate existing inequities but instead address them.
Conclusion: A Strategic Leap Forward
Integrating generative AI with CQL offers a lot of promise, but there are hurdles the industry must address:
• Privacy and Security: Ensuring that patient data used by generative AI tools remains secure and compliant with regulations.
• Training and Adoption: Educating clinicians and administrators on how to effectively use AI-generated CQL to enhance workflows.
• Bias and Fairness: Continuously monitoring AI algorithms for bias and developing strategies to mitigate any detected disparities.
The healthcare industry must also prepare for internal changes, such as upskilling teams, revising data acquisition strategies and understanding the implications for patient data privacy. The path forward requires strategic planning and collaboration among stakeholders.
The integration of generative AI and CQL is not a flashy revolution; it’s a strategic leap toward a more efficient, equitable healthcare system. While fully realizing this potential requires industry collaboration, careful planning and responsible implementation, the synergy between generative AI and CQL sets the stage for profound, long-term improvements in healthcare delivery. This isn’t just about technology—it’s about reshaping healthcare to serve patients, providers and payers in a fairer, more connected way.
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