Healthcare data interoperability remains a significant challenge, creating obstacles to effective patient care, operational efficiency, and cost management. Despite considerable technological advancements, healthcare systems continue to struggle with effective communication, frustrating healthcare payers, providers, and patients.
Agentic AI has emerged as a promising solution as the next step in artificial intelligence evolution. Unlike conventional AI systems that respond to inputs or act on rule-based protocols, Agentic AI operates autonomously to complete tasks, adapt to changing situations, and resolve complex problems without constant human oversight. Agentic AI behaves more like a co-worker than a tool: it learns, reasons, and acts on your behalf.
This technology has the potential to fundamentally transform healthcare data system interactions, establishing connections between previously incompatible platforms and enabling seamless information exchange to enhance patient outcomes, reduce operational costs, and improve efficiency across the healthcare ecosystem.
Understanding Agentic AI in Healthcare
Agentic AI refers to AI systems that operate with autonomy, adaptive reasoning, and goal-oriented execution. Agentic AI marks a significant evolution in artificial intelligence technology, particularly within healthcare. UiPath believes Agentic AI is the natural evolution of automation: where AI agents can “pursue a goal, collaborate with other agents or systems, and adjust course based on outcomes—without requiring continuous human oversight” (UiPath, 2024).
What is an orchestration framework?
An orchestration framework is a system that automates and coordinates complex tasks across multiple environments. It ensures proper execution sequence, timing, and dependency management while handling errors and allocating resources.
Unlike traditional AI systems that operate within fixed parameters (e.g. document reading) or generative AI (e.g. ChatGPT) that creates content based on prompts, Agentic AI is task-driven. It leverages orchestration frameworks, APIs, structured prompts, and memory to pursue objectives across multiple steps and data sources. In healthcare, this unlocks powerful new capabilities, including:
- Autonomous decision-making: These systems can independently evaluate situations, assess options, and implement appropriate actions within preset guidelines.
- Real-time adaptability: Agents respond to new data and environmental shifts in real time.
- Problem-solving without human intervention: Agents can identify and resolve issues independently within predefined boundaries.
- Cross-system task orchestration: example, pulling data from an EHR, validating eligibility in a payer portal, and sending personalized outreach—all autonomously
- Learning from previous results: Systems continuously improve by analyzing outcome data.
Real-world implementations of Agentic AI are already emerging throughout healthcare. For instance, the UK’s National Health Service is using AI agents in a breast cancer screening program. The AI system analyzes mammograms, adjusts real-time detection criteria, and learns from radiologists’ corrections. The goal is to detect malignant tumors earlier and with more consistency than is currently possible (Nature Medicine, 2023).
Why Healthcare Interoperability Still Fails
The healthcare sector continues to struggle with data interoperability challenges that hinder effective care delivery and operational efficiency. The ONC reports that only 43% of U.S. hospitals routinely engage in all four domains of interoperability: sending, receiving, finding, and integrating data (ONC, 2023).
Key Barriers Include:
Standardization Struggles
A wide variety of data formats and structures continue to be used in healthcare applications and data management systems, making seamless integration daunting. While standards like HL7 and FHIR exist, their adoption remains inconsistent across organizations, undermining the goal of interoperability.
Data Quality Dilemma
The effectiveness of data integration depends on the quality of shared information. When health data is inconsistent, incomplete, or inaccurate, it creates barriers to meaningful information exchange between systems. Even when technical compatibility exists between platforms, poor data quality can render shared information misleading or unusable for clinical decision-making.
Limited EHR Interoperability
Despite widespread investments in electronic health record (EHR) systems, standardization challenges continue, even among solutions from the same vendor. Recent findings from the Office of the National Coordinator for Health Information Technology’s (ONC) 2023 report indicate that while 70% of hospitals participated in all four domains of interoperable exchange (sending, receiving, finding, and integrating data), only 43% engaged in such exchange regularly.
Security and Privacy Concerns
Enhanced data sharing inherently increases cybersecurity vulnerabilities and privacy risks. Healthcare organizations must carefully balance their information exchange needs with their responsibility to protect sensitive patient information, often resulting in overly conservative sharing policies that hinder true interoperability.
Accessibility Issues
Even when data-sharing capabilities exist, making complex clinical information understandable and accessible to patients remains problematic. Technical terminology and medical jargon often create barriers preventing patients from meaningfully engaging with their health information.
Financial Barriers
Implementing robust interoperability solutions requires substantial investment in technological infrastructure, software platforms, and specialized expertise. The Office of the National Coordinator has identified this as a critical challenge.
Regulatory Complexity
Compliance requirements related to data sharing continue to evolve as new technologies become available. This complex regulatory environment adds another layer of difficulty to interoperability initiatives. Healthcare organizations are navigating regulations, including HIPAA, GDPR, and state-specific privacy laws, each with unique requirements and potential penalties for non-compliance.
Organizations that fail to implement robust compliance frameworks risk financial penalties, with HIPAA violations alone potentially resulting in fines up to $2.1 million per year. Forward-thinking providers are addressing these challenges with comprehensive governance structures and dedicated compliance officers who continuously monitor regulatory changes and update policies accordingly.
How Agentic AI Addresses Interoperability Challenges
Agentic AI offers solutions to healthcare interoperability challenges through the autonomous integration of disparate data sources, workflow optimization, and enhanced real-time decision support. Here’s how:
UiPath best practice: Agentic AI uses long-running workflows and resumable states to manage complex, multi-system healthcare tasks (UiPath Healthcare Playbook, 2024).
Data Integration & Unification
Agentic AI consolidates fragmented information from EHRs, laboratory systems, wearable devices, and imaging platforms into a unified interface. Advanced natural language processing and machine learning capabilities extract meaningful insights from unstructured clinical notes and present them in actionable formats, reducing manual data searching.
Automated Workflows
Agents further streamline automated administrative processes like claims processing and prior authorizations by:
- Claims Processing: Validating eligibility, identifying discrepancies, and reducing approval times.
- Prior Authorizations: Autonomously evaluating clinical criteria and required documentation, resulting in reduced review cycle times.
- Data Reconciliation: Automatically cross-referencing claims data with financial records to identify and resolve payment discrepancies, decreasing manual reconciliation efforts.
Care Coordination
By analyzing EHR, scheduling, and case management data in real time, agents can surface high-risk patients and proactively initiate interventions.
- Trigger follow-ups post-discharge
- Flag gaps in care for diabetic or CHF patients
- Automatically alert PCPs and patients when appointments are missed
Microsoft AI Impact: Microsoft reports that AI-powered orchestration reduced 30-day readmission rates by 15% in partnered health systems using Dynamics and Azure Health Bot (Microsoft Health Futures, 2023).
Interoperability Enhancement
Agentic AI can act as an intermediary between various APIs and databases, facilitating seamless data exchange across non-standardized healthcare systems, including specialty areas like oncology and radiology. The agent continuously retrieves and reconciles real-time patient information from multiple sources while automatically flagging data inconsistencies for resolution.
There is no need to “rip-and-replace.” Agents can interpret legacy formats, map data to FHIR, and synchronize with cloud-native platforms.
Personalized Care
By analyzing comprehensive patient data, including medical histories, genetic profiles, and lifestyle factors, Agentic AI supports the creation of tailored care plans while monitoring treatment adherence to improve outcomes.
Ultimately, Agentic AI will transform healthcare interoperability by unifying fragmented data, automating complex workflows, and enabling proactive care coordination – all while helping reduce clinician burnout and improving efficiency for both payers and providers.
What are the Future Implications for Healthcare?
The continued evolution of Agentic AI will shape several key trends in healthcare interoperability:
AI Adoption Timeline
The implementation pace of Agentic AI in healthcare is expected to outpace updates to regulatory frameworks. Healthcare organizations should take a proactive approach to adopting this technology while collaborating with regulatory bodies to develop appropriate governance structures.
Data Quality Awareness
The proliferation of Agentic AI will drive increased focus on data standardization, normalization, and overall usability. Organizations will increasingly recognize that AI system effectiveness depends on data quality, leading to greater investments in data governance initiatives.
Shift Toward Value
Healthcare markets are evolving beyond basic connectivity to focus on generating actionable insights. Agentic AI will accelerate this shift by not only connecting disparate systems but also extracting meaningful, implementable intelligence from integrated data sources.
FHIR Adoption Momentum
The Fast Healthcare Interoperability Resources (FHIR) standard will continue gaining widespread adoption, supported by Agentic AI systems that facilitate seamless integration between legacy platforms and FHIR-compliant systems.
Regulatory Considerations
Regulatory bodies, including the FDA, will continue refining their frameworks for autonomous AI systems in healthcare. This evolution will focus on balancing innovation with patient safety, establishing clear pathways for responsible Agentic AI deployment.
The Big Picture
Agentic AI is the primary key for addressing healthcare interoperability challenges. Through the autonomous bridging of system gaps, these intelligent agents are fulfilling the dream of safe healthcare data sharing, access, and utilization. However, several factors must be addressed to ensure system-wide success. Healthcare organizations must tackle data accuracy concerns, privacy considerations, and trust-building in autonomous systems. Successful implementation requires responsible deployment with safeguards that align AI capabilities with payer, provider, and patient/member objectives.
Despite these implementation challenges, the potential of Agentic AI cannot be overlooked. Healthcare organizations must proactively explore and identify strategic use cases where AI agents can address their most critical interoperability challenges. Success will depend on organizations planning strategic, phased implementation, beginning with small, localized applications before broadening scope. This approach will mitigate implementation risks and create early wins to generate organizational buy-in. The goal goes beyond mere technological adoption to generating a sustainable evolution that unifies the needs and priorities of payers, providers, and patients alike.
What to do Now
Agentic AI is no longer an emerging technology—it’s a strategic imperative. Health systems, payers, and solution vendors that move first will gain operational advantages, reduce administrative burdens, and improve both clinician and patient experiences.
Here’s how to get started:
- Identify a Strategic Friction Point
Choose a process where data fragmentation, manual handoffs, or inefficiencies delay outcomes, such as prior authorization, claims reconciliation, or care transitions.
- Start with a Small Pilot
Deploy a limited-scope Agentic AI solution using platforms like UiPath to:
- Orchestrate tasks across systems
- Include human-in-the-loop oversight
- Measure KPIs: time saved, tasks completed, error rates, and user satisfaction
- Build a Cross-Functional Governance Team
Bring together IT, compliance, clinical, and operations to align goals, set boundaries for agent autonomy, and monitor outputs.
Best Practice: Align AI deployment with your organization’s existing risk management and change control frameworks (Microsoft Responsible AI Standard, 2025).
- Scale with Confidence
Once value is demonstrated, broaden scope to additional workflows and build a roadmap to enable full interoperability maturity over time.
