Conversational GenAI Querying Solution for Engaging Insights

Background:

The healthcare industry increasingly depends on data to drive clinical decisions, improve patient outcomes, streamline operations, and meet regulatory requirements. Effectively leveraging this data has become a strategic priority across health systems, payers, and care teams. However, this goal is often undermined by the limitations of current data systems in handling the scale, diversity, and fragmentation of healthcare data. Information is frequently siloed across EHRs, claims, labs, and imaging systems, making it difficult for users to access unified patient insights. This places an undue burden on clinical, operational, and administrative users to write complex queries that require deep technical knowledge of data structures and sources. As a result, valuable time is spent navigating data complexity rather than acting on insights. Existing tools offer limited support for these challenges, leading to inefficient data use, low returns on data investments, and high administrative overhead for users who are not data specialists.

Key Capabilities:

• Semantic Querying

The deCypher solution leverages GPT-based models to accurately interpret the semantic intent behind natural language queries. It is contextually tuned to understand the nuances of clinical terminology, healthcare workflows, and patient-centric scenarios—enabling users to extract meaningful insights from complex healthcare data without needing deep technical expertise.

• Query Optimization

deCypher continuously refines queries in collaboration with users, using algorithms to optimize and deliver precise, relevant results. It also provides smart recommendations based on past interactions, helping users access healthcare data more efficiently.

• Data Integration

deCypher provides the capability to seamlessly integrate diverse healthcare data types, including claims, patient records, and benefit information. deCypher accomplishes this by:

 Breaking down the query into data elements required and making an informed estimate of the datasets where those elements would reside.  These data elements reside in disparate data structures. deCypher supports multiple data structures commonly found in healthcare systems.  These data elements differ in data types, which may include text, images, and structured data. deCypher synthesizes these data elements into composite insights for improved decision-making

• Adaptive Learning

deCypher is by nature a learning and AI driven solution. Powered by foundation models, the system continuously adapts to evolving language patterns and user preferences over time. The learning is not limited to semantics but is incorporated in every layer of the solution, from query generation to data synthesis to better serve and adapt to the needs of the end user. To summarize, deCypher provides an intuitive interface for interacting efficiently with complex datasets using natural language and extracting actionable insights.

The solution employs the following Azure services- Azure AI Foundry, AI Search, CosmosDB, API Management, Azure CDN, Azure Functions, Blob Storage etc.

Highlights: • A single-point language model driven solution that enables accurate semantic understanding, seamless data integration across sources and an adaptive interface for users to interact intuitively all at the same time • Facilitates the discovery of hidden patterns in patient data, enabling more informed decision-making in areas like clinical care, patient outcomes, and personalized treatment plans. • With continuous learning and customization capabilities, information retrieval and decision-assistance keep getting better with every iteration

The solution employs the following Azure services- Azure AI Foundry, AI Search, CosmosDB, API Management, Azure CDN, Azure Functions, Blob Storage etc.