Enhancing Payer prior authorization workflows

Gen AI in healthcare

Generative AI (Gen AI) is rapidly emerging as a transformative force in healthcare, reshaping both clinical and operational domains. One of the primary drivers of this transformation is Gen AI’s ability to handle the industry’s vast volumes of unstructured data—the kind that underpins critical decisions but traditionally resists automation.

This data is multimodal, appearing in formats such as documents, images, notes, clinical guidelines, and ontologies. Despite multiple attempts to harness this information, automation efforts often fell short due to the data’s complexity and lack of structure. Gen AI, however, is purpose-built to process unstructured, multimodal content. It uses advanced language, vision, and multimodal models to unify disparate data, accelerating the development of AI applications in healthcare. This capability is transforming care delivery, operational efficiency, and the ability to scale personalized care.

The promise vs the pitfalls

While Gen AI has shown immense potential, from enhancing productivity to improving clinical outcomes, its real-world performance has been inconsistent. Instances of biased, incorrect, or hallucinated outputs have raised concerns about transparency and explainability.

Healthcare leaders, organizations, and prominent analysts, including Gartner, are urging the development of trusted Gen AI applications built on safety principles and compliant design. However, the lack of standardized ways to assess Gen AI’s performance, reliability, and compliance makes trust-building difficult. Most existing frameworks remain theoretical and lack operational guidance.

Moreover, fragmented implementations—driven by urgency rather than governance—have created integration and oversight challenges. Siloed data, disconnected workflows, and increasing risks around privacy and data misuse further complicate scalable deployments in healthcare.

It is no debate that trust, quality, and predictability must become foundational to any Gen AI deployment. Without them, these models will remain experimental and unfit for production.

Introducing the Quality & Trust framework

CitiusTech’s Generative AI Quality & Trust (Q&T) Solution is a first-of-its-kind approach that addresses these gaps. It offers a decision-making framework and software libraries that help teams choose the right quality and trust metrics for specific healthcare use cases.

The framework takes a layered approach, starting from business outcomes and mapping corresponding quality metrics down to the application stack. It evaluates LLM response quality, data accuracy, hallucination, toxicity, bias, and efficiency.

By tying Gen AI performance to real business impact, the Q&T solution ensures that AI applications are innovative, reliable, scalable, and production ready.

This trust-centric approach becomes even more critical when Gen AI is applied in high-stakes, compliance-driven environments like Payer prior authorization workflows. Its technical scalability is strengthened through its deep integration with Snowflake's native services, ensuring performance, flexibility, and enterprise readiness.

“Lack of the right frameworks and metrics to evaluate Generative AI applications on the vectors of quality, trust, reliability, and efficiency have hindered adoption, scaling, and deployment of Gen AI in enterprises. We have built our Quality and Trust Solution to address this gap, specifically for Gen AI applications in healthcare. In healthcare, context is everything and our solution ensures context aware quality assurance and risk management. Engineering quality and reliability in Gen AI builds trust. Snowflake's powerful and differentiated Cortex AI stack allowed us to architect the solution for performance and adaptability that engenders transparency and compliance - a must have for healthcare.”
— Kaushik Raha, Vice President – AI & Analytics, CitiusTech

Real-world use case: Transforming prior authorization

In practice, timely, accurate prior authorization (PA) is essential for controlling costs and ensuring care delivery. However, the process today is resource-intensive and error-prone, with manual document review, long turnaround times, and administrative overload.

CitiusTech’s Generative AI-powered Prior Authorization solution, built using Snowflake's Cortex AI, addresses these pain points. It empowers Utilization Management staff and clinical reviewers to process PA requests more efficiently by summarizing members’ clinical history and generating relevant responses based on clinical guidelines.

What makes this solution enterprise-ready is its integration with both the Q&T framework and Snowflake's powerful, cloud-native ecosystem. This ensures that every Gen AI-driven output - whether it's a summary, a recommendation, or a decision letter is evaluated for reliability, transparency, and adherence to business and clinical expectations.

“CitiusTech's Quality & Trust solution, built on Snowflake's robust and scalable platform, represents a breakthrough in enterprise-grade AI deployment. By combining advanced governance frameworks with Snowflake's features and Cortex AI capabilities, it delivers what healthcare organizations have been seeking — a trusted, scalable approach to implementing Generative AI. The solution's seamless deployment on Snowflake ensures not just technical performance, but also the reliability, transparency, and compliance that healthcare demands. This is exactly the kind of thoughtful architecture we need to move AI from experimental to production-ready in the healthcare setting.”
— Murali Gandhirajan, Global Healthcare and Life Sciences CTO, Snowflake

Solution architecture deep dive

Gen AI Prior Authorization Solution Architecture on Snowflake

The architecture is divided into two key stages:

Fig 1: Data preprocessing and embedding

Step 1: Data preprocessing and embedding

An event-driven pipeline preprocesses clinical documents and generates vector embeddings using Snowflake streams and tasks, enabling near real-time ingestion from sources like AWS S3 or Azure ADLS.

Fig 2: Prompt construction and inference

Step 2: Prompt construction and inference
Cortex LLMs generate summaries, recommendations, and clinical documents using Retrieval-Augmented Generation (RAG) with persistent chat history and contextualized Q&A.

Leveraging Snowflake Cortex AI

Snowflake Cortex AI provides a managed, scalable environment for deploying Large Language Models (LLMs) and powering Retrieval-Augmented Generation (RAG) applications. It enables efficient hybrid search across structured and unstructured data within Snowflake’s ecosystem.

Through Cortex AI, healthcare organizations can build AI applications with:

• Native integration into Snowflake's data warehouse
• Low-latency, high-accuracy search
• Easy orchestration of AI pipelines using familiar SQL and Snowpark constructs

This makes it easier to combine AI reasoning with governed healthcare data for real-world deployment. 

Deploying the Q&T solution on Snowflake

The Q&T solution is hosted on Snowpark Container Services and deployed as APIs that integrate directly into Gen AI applications built on Snowflake.

Key components include:

• Q&T Backend Service: Meta-data and core logic
• Q&T Evaluator Service: Advanced LLM-based metric evaluation
• Q&T Dashboard Service: Interactive visualization of performance metrics

This deployment model enables seamless observability, transparency, and governance across every Gen AI use case, while ensuring optimal performance and cost-efficiency on Snowflake.

The diagram below illustrates the deployment architecture of the integrated Q&T and Prior Authorization solution within Snowflake’s ecosystem. It leverages containerized services, FastAPI, and Snowflake Cortex LLMs for scalable performance.

Fig 3: Q&T and Prior Authorization solutions on Snowflake

Accelerating Gen AI with SwiftKV Optimization

CitiusTech uses SwiftKV-optimized Llama 3.3 70B models to improve inference speed and cost efficiency. This results in up to 75% cost savings compared to standard Meta Llama models, making enterprise-scale AI more accessible.

When hosted within the Snowflake environment, this optimization delivers real-time responses while maintaining enterprise-grade accuracy and trust standards. 

Conclusion

CitiusTech’s Prior Authorization solution, underpinned by its Quality & Trust framework and built on the robust infrastructure of Snowflake Cortex AI, redefines Payer workflows. By embedding governance, transparency, and performance assurance at every layer of the stack, the solution delivers scalable, trustworthy Gen AI applications. This translates to reduced reviewer burden, faster approvals, and more accurate decision-making, advancing the goal of delivering high-quality, personalized care at scale.