Identifying Social Determinants of Health Barriers Automation

Manual screening for Social Determinants of Health (SDOH) barriers is slow and misses critical enrollment risks. A custom automation workflow analyzes structured EHR fields, clinical narratives, and external data (ZIP code indices, public records) to flag patients at risk of dropout due to transportation, financial toxicity, or health literacy gaps. This proactive detection shifts support from reactive problem-solving to pre-emptive intervention, directly improving cohort diversity and securing hard-won patient slots by addressing barriers before they cause attrition.

Implementation integrates with Epic or Cerner via HL7/FHIR, uses NLP agents for narrative analysis, and routes high-confidence flags to platforms like Unite Us or Healthify via API. Controls include confidence thresholds, mandatory human review for edge cases, and audit logs linking flags to support actions. The architecture reduces screening labor by 70% and cuts pre-enrollment attrition by identifying and resolving barriers during the consent process, not after the first missed visit.

This workflow automates the identification of transportation, financial, and literacy barriers that cause patient dropouts, directly impacting trial diversity and cost. It ingests structured EHR data (Epic, Cerner), unstructured clinical notes, and external data APIs (census, public transit) into a harmonized patient profile. A rules engine and NLP agents then flag high-risk SDOH indicators, such as inconsistent address history or mentions of financial hardship, converting manual chart review into a systematic, scalable screening process that surfaces actionable support needs.

Implementation requires orchestrating LangGraph agents for document parsing and API calls, with results routed to a human review queue in a CTMS like Veeva. Controls include confidence scoring for each flag, mandatory review for high-impact barriers, and audit logs for compliance. The architecture reduces manual screening labor by 70% and creates a closed-loop system where patient navigators receive prioritized lists, enabling timely intervention before enrollment is jeopardized.

Phase 1 establishes the data ingestion and fusion layer, integrating structured EHR data (Epic, Cerner) with unstructured clinical notes and external SDOH data sources via API agents. The checkpoint is a validated data pipeline producing a unified patient profile with initial SDOH flags (e.g., transportation codes, financial class). This foundational phase de-risks the project by proving data accessibility and quality before complex logic is applied, directly targeting the manual data aggregation bottleneck that delays support interventions.

Phases 2 and 3 deploy the core NLP agents for narrative extraction and the configurable risk-scoring engine, with a checkpoint measuring flag accuracy against manual review. The final orchestration phase integrates with patient navigation platforms (like Unite Us) and clinical systems, with a go-live checkpoint measuring time-from-identification-to-outreach. This phased approach ensures each layer—data, logic, action, governance—is validated for clinical safety and operational impact before proceeding, delivering measurable ROI in reduced screen failures and improved participant retention.

This workflow automates the identification of transportation, financial, and literacy barriers from patient records and external data to improve trial diversity and retention. The operational upside comes from proactively routing at-risk patients to navigation services, reducing screen failures and dropout rates. Implementation requires a multi-phase rollout, starting with a single-site pilot to validate data fusion logic, risk-flagging accuracy, and integration with patient management systems like Epic or Salesforce Health Cloud before enterprise scaling.

Critical controls include a pre-screening governance gate to validate data use agreements, mandatory human review for high-risk flags before outreach, and immutable audit logging in the CTMS. Exception routing handles data quality issues or ambiguous signals, escalating to site coordinators. Monitoring focuses on flag accuracy, intervention outcomes, and demographic bias detection to ensure the workflow expands access equitably. Rollout sequencing ties each phase to specific validation checkpoints and stakeholder sign-off.

This workflow automates the identification of non-clinical barriers that cause patient drop-out, directly impacting trial diversity and retention costs. It ingests structured EHR data (Epic, Cerner) and unstructured clinical notes, then enriches this with external API calls for geocoding, public transit access, and area deprivation indices. The core operational savings come from replacing manual social worker chart reviews with a continuous, scalable screening layer that flags at-risk patients for navigation services before enrollment stalls.

Implementation requires a LangGraph or custom orchestrator to sequence agents, with strict governance for PHI handling in API calls. The logic engine applies configurable rules (e.g., >10 miles to site + no car + high ADI = high risk) to generate a risk score. Flagged cases are routed to a human review queue in platforms like ServiceNow or a custom navigation dashboard, with all decisions logged for audit. This creates a closed-loop system where navigation interventions are tracked back to enrollment outcomes, proving ROI through improved retention rates.