Multi-Agent Artificial Intelligence Systems for Cross-Organizational Clinical Data Reconciliation: A Federated Interoperability Framework for Harmonizing Patient Records Across Disparate Health Information Networks

Sindhukumar Sundaram

doi:10.71097/IJAIDR.v17.i1.2033

Multi-Agent Artificial Intelligence Systems for Cross-Organizational Clinical Data Reconciliation: A Federated Interoperability Framework for Harmonizing Patient Records Across Disparate Health Information Networks

Author(s)	Sindhukumar Sundaram
Country	United States
Abstract	As healthcare data exchange expands through frameworks such as the Trusted Exchange Framework and Common Agreement (TEFCA), Carequality, and CommonWell, integration engines increasingly receive clinical data for the same patient from multiple organizations — each using different electronic health record (EHR) systems, coding standards, clinical documentation practices, and data quality levels. Reconciling these disparate representations into a coherent, trustworthy clinical picture is a fundamentally unsolved problem at scale. Existing approaches rely on deterministic matching rules and manual clinical review, which do not scale across the volume and variety of cross-organizational exchanges now emerging in production health information networks. This paper proposes the Multi-Agent Reconciliation Architecture (MARA) — a system of five specialized artificial intelligence agents operating at the integration layer, each responsible for a specific reconciliation domain: patient identity matching, medication list harmonization, problem list deduplication, allergy cross-referencing, and clinical document provenance verification. These agents operate collaboratively through a structured negotiation protocol to produce a unified, confidence-scored reconciliation output for each patient encounter. Evaluation on a synthetic cross-organizational dataset — four independent health systems, 100,000 patients, deliberate inconsistencies — demonstrates 92.4% overall reconciliation accuracy, with medication harmonization achieving 95.1% and problem list deduplication achieving 87.3%. The multi-agent negotiation protocol resolves 78% of inter-agent conflicts without human escalation. End-to-end processing averages 2.8 seconds per patient record across four contributing organizations.
Keywords	multi-agent systems, clinical data reconciliation, federated interoperability, patient matching, health information exchange, artificial intelligence, TEFCA, terminology harmonization, medication reconciliation, FHIR
Field	Engineering
Published In	Volume 17, Issue 1, January-June 2026
Published On	2026-05-08
DOI	https://doi.org/10.71097/IJAIDR.v17.i1.2033

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Multi-Agent Artificial Intelligence Systems for Cross-Organizational Clinical Data Reconciliation: A Federated Interoperability Framework for Harmonizing Patient Records Across Disparate Health Information Networks

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