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NIX United helped a US-based healthcare startup build a HIPAA-compliant, multi-agent clinical assistant that enables clinicians to access patient data and clinical insights.
Healthcare
AI, Data Science, Chatbot, AI Agent, AI Consulting
LangChain, LangGraph, Vertex AI, neo4j, React
The client, a US-based healthcare startup, focuses on technical support for clinicians in the gynecological sector. Recognizing the lack of attention to women’s health and growing demand for fast, data-driven decision support, they decided to develop a sophisticated AI-powered web application that integrates seamlessly with existing electronic health record (EHR) systems. The application’s core functionality was supposed to be an intelligent chatbot that provides clinicians with real-time patient data, curated medical research, and the latest clinical protocols at the point of care.
However, to bring such an inspiring idea to life, the client required additional capacity and technical expertise, so they turned to NIX United. Our proven track record in the highly regulated healthcare industry and our experience in engineering advanced AI-driven solutions made us a game-changer when the client chose us as their partner.
Since the efficiency of an AI-driven application is directly proportional to the quality and volume of the data, we faced a critical challenge in eliminating data gaps caused by scattered data across the EHR. These data gaps threaten to degrade the model’s performance, so we need to address them efficiently and rapidly. We not only gathered all the data but also moved toward in-house HIPAA-compliant data hosting and more granular control over data processing and validation.
In the very beginning, the client had only conceptual research and initial ideas, which we transformed into a production-ready, HIPAA-compliant application. Developed from the ground up, the solution is a standalone application deployed on Google Cloud that integrates seamlessly with the client’s Athena EHR via secure APIs.
The core of the solution is an AI-powered layer between the clinician and the EHR system, consisting of a diverse set of AI agents, LangChain integration and Google Vertex AI (Gemini). To manage this multi-agent system and navigate the entire solution with precision and ease, we designed a comprehensive orchestration tool based on LangGraph and Neo4j graph database management that ensures patient data retrieval and analysis are both secure and grounded in factual EHR records.
We decided to implement an agentic solution using the ReAct frameworks, which support structured logic, modular expertise, and controlled tool use rather than simple text generation. As a result, agents can dive deep into patient conditions, mirror diagnostic reasoning, and maintain separation of concerns and auditability. Meanwhile, the ReAct agents reduce hallucination risks and increase factual grounding through interaction with third-party tools. The multi-agent system also goes beyond data retrieval, transforming raw patient data and reported symptoms into actionable medical intelligence.
The client received a full-fledged application delivering a dual-layered performance model designed to bridge the gap between traditional record-keeping and advanced predictive analytics:
While the project is still in the production and refinement phase, the product has already engaged a select group of clinicians who are utilizing and testing the app in live environments. In the next iterations, our roadmap focuses on scaling the solution by integrating a broader range of diverse EHR systems and expanding the client’s footprint across a wider network of clinics.
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