Electronic Health Records (EHRs) are now used by over 94% of hospitals in the U.S. This high use brings both good opportunities and some problems. Many old systems, different data formats, and varying IT skills cause health data to be scattered. This makes it hard for clinical departments, labs, insurance companies, and outside specialists to share information. When systems cannot connect well, patient care can be delayed, administrative work grows, and data mistakes increase.
Hospitals lose about $262 billion each year partly because of denied insurance claims and poor data exchange. These money problems and the need for better patient care require systems that can safely share correct, real-time data and also support advanced tools like AI.
To fix these problems, healthcare groups are using standards like HL7 FHIR and SMART on FHIR. These create a common way for different health systems to talk and work together.
HL7 FHIR is a modern standard made by Healthcare Level 7 International. It helps different computer systems share health data using internet methods many IT workers already know, like RESTful APIs, JSON, and XML. Unlike older HL7 methods that were message-based and hard to use, FHIR lets apps get real-time access to small pieces of health info called resources. These include things like patient details, clinical observations, medications, and care plans.
In the U.S., laws like the 21st Century Cures Act and TEFCA say that EHR data must be available through APIs to help patients and health technology development. FHIR’s API design lowers tech barriers and speeds up the use of apps that need live health data.
SMART on FHIR works on top of FHIR by adding security and permission tools. It lets third-party apps safely connect with EHR systems. Using OAuth 2.0 protocols, SMART on FHIR lets doctors and patients approve apps to see their health data without risking privacy or system safety. This setup allows new health apps to work with regular clinical systems while following HIPAA and other privacy rules.
AI tools need steady, timely, and organized data to work well. AI helps with predicting health issues, supporting decisions, automating notes, and engaging patients. But if many clinical systems use different formats, the AI might make mistakes or not work well.
Standards like HL7 FHIR and SMART on FHIR help AI in these ways:
An example is Microsoft’s healthcare agent orchestrator. It links HL7 FHIR EHRs and Microsoft Fabric, a health data platform, to help automate tasks like tumor board notes. This shows how AI uses standards to save time for clinicians.
AI is changing healthcare by automating simple office and clinical jobs. Tools like Natural Language Processing (NLP), machine learning, and robotic process automation (RPA) are used more to make work faster and cut down human mistakes.
For example, AI-powered front-office tools like Simbo AI handle phone calls, appointments, insurance checks, and patient questions. This helps reduce staff work and errors from typing mistakes.
For clinical tasks, AI can automate reviewing charts, writing discharge orders, and making clinical notes. When combined with EHRs using FHIR and SMART on FHIR, AI can give doctors real-time decisions, risk warnings, and alerts while keeping data safe.
AI-driven data analysis and automation can speed up decisions and improve patient care. Research shows that combining AI with good teamwork and leadership helps health organizations change how they work.
But to use AI well, challenges like old system compatibility, data differences, and strict rules must be handled. HL7 FHIR helps by offering standard data models so automation tools face fewer problems and have clearer results.
In the U.S., protecting patient data and following rules like HIPAA, the 21st Century Cures Act, and CMS rules is very important when sharing data or using AI.
HL7’s FAST Security Implementation Guide (IG) will be needed for TEFCA FHIR exchanges starting January 1, 2026. It sets strong security rules and uses modern methods like OAuth 2.0 and OpenID Connect, plus dynamic client registration with digital certificates. These help build a safe system where AI apps can access and process sensitive health data.
Dynamic client registration and JSON Web Token (JWT) authentication make it easier and faster for health groups to add many AI apps without losing security. The tiered OAuth system supports both patient-facing apps and backend clinical workflows with different access options.
The FAST Consent project is also working to create standard tools for handling patient privacy choices across many systems. This makes sure AI apps follow patient permissions when accessing data.
Medical practice leaders and IT managers can gain many benefits by using HL7 FHIR and SMART on FHIR:
Even with benefits, adding HL7 FHIR, SMART on FHIR, and AI is not always easy. Different IT systems, old software, and company policies can slow things down. Some EHR vendors implement FHIR differently, and clinical terms may not always match.
A good plan helps address these issues:
As healthcare goes more digital, standardized data exchange will become even more important. HL7 FHIR is growing with new updates like R5, which add clinical decision help, billing, and query features. This makes it better for complex AI uses.
Global projects like FAST Security and International Patient Access aim to make standards work worldwide. This supports health work and AI systems across countries.
Medical practices in the U.S. will benefit by preparing early. Building systems with FHIR and SMART on FHIR will help make AI use safe, efficient, and scalable. This will improve managing practices and caring for patients over time.
The healthcare agent orchestrator is a system available in Azure AI Foundry Agent Catalog featuring pre-configured and customizable AI agents that coordinate multimodal healthcare data workflows, such as tumor boards, to augment clinician specialists by automating tasks that typically take hours, thus improving healthcare enterprise productivity.
It connects via HL7 FHIR standards and SMART on FHIR frameworks, enabling secure, authorized access to EHR data using OAuth2 tokens. The orchestrator uses patterns like SMART Backend Services to authenticate and query clinical data through APIs for seamless integration with existing healthcare systems.
Challenges include variability in data formats, interoperability differences, legacy systems lacking FHIR support, performance scalability constraints, distribution of patient data across multiple systems, and strict compliance, privacy, and security requirements.
HL7 FHIR is a standardized, resource-based framework for healthcare data exchange that supports RESTful APIs, enabling flexible and developer-friendly interoperability across diverse healthcare systems. It is essential for enabling modern AI applications to access structured clinical data efficiently.
Three key patterns: User authorization via SMART scopes for clinician-authorized access, backend service integration for system-level workflows without user interaction, and patient-authorized app launch allowing patients to directly authorize apps to access their health data.
When invoked, the Patient History agent uses the MCP server’s data access layer to authenticate and query the FHIR service, fetching patient resources and clinical notes (DocumentReference). The gathered data is then processed by AI agents to generate draft tumor board content for clinician review.
Microsoft Fabric offers unified data management by harmonizing healthcare datasets, supports multi-modal data ingestion, advanced analytics including AI enrichments, and compliance with standards like FHIR and regulations such as HIPAA, serving as a scalable data platform for healthcare AI applications.
Notable patterns include Microsoft Fabric User Data Functions (reusable code endpoints exposing subsets of data with flexible business logic) and the Fabric API for GraphQL (enabling precise, aggregated queries across multiple highly related healthcare datasets), both facilitating efficient AI data access.
Standardization, via HL7 FHIR and SMART on FHIR, ensures interoperability, security, compliance, and scalability, allowing AI agents to reliably access, interpret, and coordinate diverse healthcare data sources consistently across institutions and platforms.
It is intended solely for research and development, not for direct clinical deployment or medical decision-making. Users assume full responsibility for verifying outputs, regulatory compliance, and necessary approvals for any clinical or commercial application.
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