eff AI
PoC = k Edge × (k FHIR + k MCP + …)
AI is particularly effective at the point of care when real-time, standards, and context work together intelligently.
. Fully FHIR®-native ecosystems realized: networked, worry-free edge backends with a high-performance FHIR® validator, flexible management of any terminology provider (SNOMED CT, LOINC, ICD-10, and more), seamless integration with legacy systems, user and role management, and much more – ideal for embedded systems (e.g., medical devices, laboratory systems, mobile care robots), cluster environments, and serverless architectures.- FHIR® expertise in a complete toolkit for networked, worry-free edge backendsThe world’s only toolkit that is uncompromisingly 100 % FHIR®-compliant and, with its plugin architecture, enables flexible and future-proof digital health projects. Interfaces, logics, terminologies, databases, agents, and more can be added or removed via plugins – precisely matched to requirements and available resources.
- Tools for client developers and adminsFrom rapid prototyping to professional, custom and auditable applications, a comprehensive suite of tools is available – secure and ideal for AI Copilot–powered development, with seamless point-of-care availability.
- On-site Business Logic DevelopmentThe HealthFireKit™ architecture enables you to develop and test business logic directly within the application on-site—AI-assisted, for example. As scalability or security requirements increase, it can be seamlessly shifted to an edge-backend plugin—without having to abandon the web technology.
Instruct instead of programming – intelligent agents take over.
Instructions can be delivered via speech, text, sketches, diagrams, code, standardized FHIR® data, examples, or concepts—alone or in combination. AI agents then take on specific tasks, such as analyzing complex patient data, integrating current research, or generating personalized treatment plans. Rule-based, transparent, and efficient in the background, the agents analyze data, identify correlations, prioritize findings, and derive actionable, traceable recommendations. Results are structured and delivered at the desired level of detail. In distributed clusters, agents share information, complement and monitor each other, and coordinate complex processes.
Rule-based access to deterministic data by AI agents occurs in the appropriate context, for example using MCP (Model Context Protocol).Understanding health individually. Using knowledge.
AI agents could play an important role in making medical knowledge and research not only more understandable, but also more accessible – for example, in the context of personal digital health records. They have the potential to support individuals in active health management, accompany prevention and treatment, and provide helpful guidance amid an ever-growing flood of health information.
The healthcare system is facing major challenges – not only due to the increasing volume of information, but also because of workforce shortages and increasingly complex disease patterns. AI agents can help relieve pressure by structuring data, supporting clinical decisions, and enabling more coordinated, efficient, and personalized care.