Arpan Saxena is the Product Manager at basys.ai (based at Harvard University), a leading healthcare AI solutions company.
As AI makes headlines for its advances in creative fields and business automation, its potential to reshape healthcare continues to emerge. Clinical Quality Language (CQL), for example, offers a potential area of 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 healthcare IT systems to encode clinical guidelines and quality measures, ensuring consistent interpretation across different healthcare platforms. Organizations like the Centers for Medicare and Medicaid Services (CMS) and other clinical data standards groups have used CQL to automate reporting of quality measures and clinical decision support.
Integrating generative AI with CQL can enable healthcare providers to leverage AI without the need for extensive technical training. Typically, writing CQL requires specialized skills. However, generative AI offers clinicians the ability to articulate their reasoning in a natural language that the 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 achieve these results.
Improve 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 implemented across electronic health records (EHRs), clinics, payers, and hospitals.
Organizations are already testing the waters by combining generative AI and interoperable frameworks to improve care coordination. This trend highlights the importance of creating AI-generated insights that are more than just isolated findings: they are part of an ongoing, collaborative healthcare ecosystem. This information, when encoded with CQL, becomes actionable across multiple platforms, eliminating barriers that hinder effective data sharing.
Reduce administrative burden
Administrative tasks are a significant source of burnout for clinicians, 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 coding decisions in CQL, organizations can reduce the time spent on manual paperwork and redirect that time to patient-facing activities.
However, it is important to note that the implementation of these technologies is not without difficulties. Healthcare organizations must ensure that AI-generated CQL complies with local and state regulations, and rigorous validation processes must be maintained to ensure accuracy and reliability. This balance between automation and monitoring is crucial to successfully integrating AI into daily operations.
Supporting value-based care
Value-based care is an ambitious goal for health systems, focusing on patient outcomes rather than procedure volume. Generative AI and CQL can enable real-time, data-driven care models that directly connect care delivery to outcomes-based reimbursement models. This integration enables healthcare providers to adopt personalized, outcomes-driven care plans and ensures these plans are CQL-coded to seamlessly support reimbursement and reporting requirements.
Some health systems, for example, have started using AI to identify high-risk patients early and propose preventive measures. When combined with CQL, these suggestions can be coded 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 closer collaboration between providers and payers, building trust and transparency.
Integrating Equity and Transparency into AI-Driven Healthcare
One of the most pressing concerns surrounding AI adoption is bias, particularly in healthcare, where disparities can have serious consequences. Integrating CQL with generative AI provides a mechanism to create transparent audit trails. These leads allow healthcare providers to check and correct potential biases in AI-generated recommendations, ensuring inclusiveness and fairness of patient care.
However, to truly embed equity, healthcare organizations must adopt strong 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 use of AI in healthcare, ensuring that advancements do not perpetuate existing inequities but rather correct them.
Conclusion: a strategic leap forward
Integrating generative AI with CQL shows great promise, but the industry needs to overcome some hurdles:
• Privacy and security: Ensuring that patient data used by generative AI tools remains secure and compliant with regulations.
• Training and adoption: Train clinicians and administrators on how to effectively use AI-generated CQL to improve workflows.
• Bias and fairness: Continuously monitor AI algorithms for bias and develop strategies to mitigate any detected disparities.
The healthcare industry must also prepare for internal changes, such as upskilling teams, reviewing 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 is a strategic step towards a more efficient and equitable health system. While fully realizing this potential requires industry collaboration, careful planning and responsible implementation, the synergy between generative AI and CQL paves the way for profound, long-term improvements in healthcare delivery. health. It’s not just about technology: it’s about reshaping healthcare to serve patients, providers and payers in a more equitable and connected way.
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