@iehr/agent

About iEHR.ai

iEHR.ai is an AI-driven, FHIR®-native, interoperable EHR and Unified Digital Health Platform (UDHP) designed to unify and modernize healthcare data management, enabling healthcare organizations to deliver smarter, more connected, and future-ready care solutions.

A Unified Digital Health Platform (UDHP) is more than just a modernized electronic health record (EHR) - it’s a comprehensive, interoperable ecosystem that brings together the full spectrum of healthcare technology into a single, intelligent framework. Think of it as a “platform of platforms,” designed to unify data, streamline operations, and empower both clinicians and patients with smarter tools.

iEHR.ai, by combining interoperability, AI, and modular architecture, it empowers healthcare providers to:

• Break down data silos
• Enhance care coordination
• Accelerate digital transformation
• Meet evolving global standards

Whether you're building a next-gen health app, integrating legacy systems, or launching AI-powered diagnostics, iEHR.ai offers the tools and infrastructure to make it happen.

• Built on HL7® FHIR standards for seamless data exchange
• Supports SNOMED CT, LOINC, ICD-10/11, and other global terminologies
• Designed for international compliance
• Integrates AI models, bots, and agents for intelligent automation
• Offers decision support, predictive analytics, and real-time data insights
• Provides developer and authentication frameworks
• Enables rapid prototyping and integration with third-party apps and services
• Includes orchestration tools like iEHR Maestro to automate workflows

To provide a free and experimental FHIR integration for iEHR Archiva, we forked an open-source FHIR library governed by the Apache 2.0 license. Our goal was to create a lightweight, adaptable foundation for testing and prototyping healthcare interoperability features.

However, the original platform required extensive modifications to align with iEHR standards and support our evolving needs in ML/AI, international FHIR compliance, and enterprise-grade deployments.

Below is a summary of the key limitations we addressed in our fork:

• US-Centric Design The original implementation was tightly bound to the US Core profile, hardcoded into its architecture. This made internationalization and support for broader FHIR profiles infeasible without significant refactoring.

• FHIR R4 Only, No R5 Roadmap
  The platform was locked into FHIR R4 with no clear upgrade path to R5. As our use cases demanded newer features and extensions, we required a more future-proof and flexible solution.

• Monolithic Architecture
  All components—including OAuth, IAM, and FHIR APIs—were bundled into a single executable. This design was incompatible with enterprise-scale deployments and conflicted with our modular, cloud-native strategy.

• Inadequate OAuth Security
  Security was a major concern. The OAuth implementation lacked essential features such as Message Signing Profiles, Pushed Authorization Requests (PAR), mutual TLS (mTLS), and Demonstration of Proof-of-Possession (DPoP), leaving critical gaps in protection and reliability.

• Limited Automation Capabilities
  While the platform offered Bots and Agents, they were implemented as simple callback functions. This approach lacked the sophistication and extensibility needed for robust automation and intelligent orchestration.

Copyright © iEHR.ai 2025

This package is forked from Medplum an open-source healthcare developer platform licensed under Apache 2.0