AI Agentic Workflow for Unifying EHR, Wearable, and Patient-Reported Data

The operational bottleneck in predictive care is data fragmentation. Clinicians waste hours manually reconciling information from Epic or Cerner EHRs, wearable streams, and patient messages, leading to delayed interventions and incomplete risk assessments. A custom agentic workflow automates this fusion, creating a single, longitudinal patient timeline. The business value is direct: reduced manual chart review by 60-80%, faster clinical decision velocity, and the foundational data layer required for accurate predictive models that prevent costly hospitalizations.

Implementation requires a pipeline built on frameworks like LangGraph for agent orchestration, with Apache NiFi or Kafka for real-time ingestion. Data is normalized to a common model (e.g., OMOP CDM) and stored in a temporal database. Critical controls include validation rules for data quality, configurable confidence thresholds for autonomous alerting, and immutable audit trails for all data transformations and agent decisions. The architecture must integrate with clinical dashboards (e.g., Epic Hyperspace) and support rollback procedures during model retraining to maintain operational safety.

This pipeline automates the costly, manual effort of synthesizing data from Epic/Cerner FHIR feeds, real-time wearable streams, and unstructured patient messages. By creating a unified, longitudinal patient record, it eliminates the clinician time wasted toggling between systems and provides the clean, contextual data essential for accurate AI-driven risk scoring and intervention logic. The operational upside is faster, more informed care decisions that reduce avoidable hospitalizations.

Implementation requires building specialized agents for normalization, parsing, and enrichment, orchestrated via a framework like LangGraph. Critical controls include data quality validation at each ingestion point, configurable confidence thresholds for automated alerts, and a mandatory human review queue for low-confidence findings or high-risk escalations. The pipeline must integrate with clinical dashboards and document all data provenance for audit and model governance.

Phase 1 establishes the foundational data pipeline, focusing on ingesting and normalizing FHIR feeds from a single EHR source like Epic. This initial layer creates the unified patient timeline, delivering immediate operational value by providing care teams with a single source of truth. It de-risks the project by proving core interoperability, data mapping, and security controls before introducing real-time complexity. This phase directly targets the manual effort spent reconciling fragmented patient data across systems.

Subsequent phases layer on real-time wearable streams and patient-reported data, followed by intelligent agents for anomaly detection. Each phase includes defined approval gates, observability dashboards, and rollback procedures. This controlled approach ensures the architecture—built on platforms like LangGraph for orchestration—can scale to additional EHRs and care protocols without destabilizing the core system, ultimately delivering the predictive insights needed for proactive care coordination.

A production-grade data fusion workflow automates the ingestion, normalization, and temporal alignment of high-velocity, multi-format health signals. It eliminates the manual labor of stitching together Epic/Cerner FHIR feeds, real-time wearable streams (e.g., Apple HealthKit, Fitbit), and unstructured patient portal messages. The operational upside comes from creating a unified, queryable patient timeline that serves as the foundation for accurate AI-driven risk scoring, care coordination, and clinical decision support, directly reducing clinician data-hunting time and improving intervention accuracy.

Implementation requires a LangGraph or Temporal-based orchestrator to manage stateful workflows across parsing agents, deduplication logic, and consent checks. Data must be staged in a governed data lake (e.g., on Snowflake or Databricks) before promotion to the operational timeline. Critical controls include automated de-identification for development, audit trails for all data transformations, and configurable approval gates for insights before they reach clinical workflows. Rollout should be phased by data source, starting with FHIR, to manage integration risk with legacy EHR systems like Cerner Millennium or Epic Hyperspace.