Case Study
AI Chat Assistant for Healthcare Platforms
An AI-powered conversational assistant that lets clinical users query PACS and RIS systems in plain language.
~2 min → ~10 sec
Average information retrieval time
High → Reduced
User training requirement
RAG + Gemini
Grounded in real backend data
Executive Overview
Healthcare IT platforms such as PACS, RIS, and Hospital Information Systems hold large volumes of
patient data behind complex, multi-screen workflows. Clinical users often struggle to quickly
retrieve relevant information or navigate those workflows efficiently. NKORR designed and
implemented an AI-powered conversational assistant that lets users interact with healthcare
systems using natural language queries, integrating Large Language Models (LLMs) with the
client's existing backend systems to deliver contextual responses, automate workflow assistance,
and improve operational efficiency.
The Challenge
In healthcare systems, four problems compound on top of each other:
- High data volume — managing and searching through thousands to millions of imaging studies.
- Complex navigation and workflows — PACS systems often have multiple screens, filters, and workflows for accessing studies and patient data, so users go through several time-consuming steps to retrieve information.
- Difficulty retrieving specific information — finding specific studies (by patient, modality, date) can require manual searching and filtering, especially for new users, increasing dependency on experienced staff.
- High dependency on user training — PACS applications typically require extensive training to use effectively, and new users struggle with features and navigation.
Our Solution
We built an AI chatbot using the Google Agent Development Kit integrated with the Google Gemini
language model. The NKORR AI Clinical Assistant introduces a conversational interface embedded
within the client's existing healthcare platform: it interprets natural language queries,
retrieves relevant clinical data from backend systems, and generates structured responses using
Retrieval-Augmented Generation (RAG) — grounding every answer in real data rather than
relying on the model alone.
- Natural language interaction with PACS/RIS systems through a chat-based interface within the application.
- Google Agent Development Kit and Google Gemini interpret user queries and generate context-aware responses.
- The chatbot integrates with existing backend APIs and database services to retrieve relevant information on demand.
- Secure, scalable communication between the chat interface, backend services, and the AI agent supports many concurrent users.
How a Query Is Answered
Every question passes through a safety check and a retrieval step before the model ever sees it.
Healthcare Use Cases
Users interact with the system in plain language instead of navigating multiple screens to find
information:
- Patient study retrieval — search imaging studies using natural language queries.
- Radiology workflow support — retrieve pending reports or modality schedules.
- Clinical knowledge assistance — surface medical guidelines and reference information.
- Operational assistance — step-by-step instructions for system tasks.
Security & Compliance
- HIPAA-compliant architecture for healthcare environments.
- Role-based access control (RBAC) for user authorization.
- Encrypted communication between services.
- Audit logging for all AI interactions.
- Protection of PHI (Protected Health Information).
Performance & Business Impact
| Metric | Before AI Assistant | After Implementation |
|---|---|---|
| Average information retrieval time | ~2 minutes | ~10 seconds |
| User training requirement | High | Reduced |
| Operational efficiency | Standard | Improved workflow productivity |
C#
TypeScript
Angular
Node.js
.NET
Microsoft SQL Server
Google Gemini
Agent Development Kit
RAG
Microsoft Azure
Work with us
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