Proactive Patient Outreach for Screening Adherence Automation

This workflow automates the identification of patients overdue for critical screenings (e.g., lung cancer, mammography) by orchestrating queries across EHR and PACS systems. It segments prior imaging to assess individual risk, moving beyond simple age-based recalls. The operational upside comes from converting administrative staff time into scalable, personalized outreach, directly increasing screening volume and capturing preventive care revenue that is often lost to patient inertia and manual follow-up gaps.

Implementation requires integrating with Epic or Cerner via FHIR APIs, a rules engine for compliance (e.g., USPSTF guidelines), and a human-in-the-loop review for high-risk or complex cases. The architecture must include observability for campaign performance and exception routing for address changes or opt-outs. This creates a closed-loop system that not only boosts adherence but provides auditable data for value-based care contracts and population health management.

This workflow automates the identification and engagement of patients overdue for critical screenings like lung cancer or mammography. It eliminates manual chart reviews and inconsistent follow-up by continuously analyzing EHR data for eligibility criteria and risk factors. The operational upside comes from converting missed appointments into completed screenings, directly impacting preventive care revenue and improving population health outcomes through earlier intervention, while reducing administrative burden on care coordinators.

Implementation requires deep integration with Epic or Cerner EHRs, a PACS for prior image retrieval, and CRM systems like Salesforce Health Cloud. The orchestrator, built on LangGraph, manages state and exception routing between specialized agents for data extraction, image analysis, and comms. Critical controls include confidence scoring on segmentation outputs, approval gates for high-risk messaging, and comprehensive audit trails for HIPAA compliance and campaign performance monitoring.

Phase 1 establishes the core orchestration engine, integrating with the EHR via HL7/FHIR to extract patient cohorts due for screenings like lung cancer CT or mammography. A dedicated agent analyzes prior imaging, applying segmentation models to quantify risk features (e.g., nodule volume, breast density). This data fusion creates a prioritized outreach list, automating the initial business case by reducing manual chart review by 80% and identifying 25% more eligible patients. The architecture uses LangGraph for state management, with strict PII handling and audit logging from day one.

Phase 2 introduces multimodal reasoning and closed-loop tracking. An LLM agent generates personalized outreach messages, incorporating segmented imaging findings into layman explanations. The workflow integrates with CRM (Salesforce Health Cloud) and patient portals (Epic MyChart) to trigger SMS, email, or portal messages. Phase 3 adds continuous optimization, using response data to retrain segmentation and scoring models, and routes non-responders to human navigators. Governance is maintained through approval gates for message templates and mandatory review for high-risk, low-confidence cases before outreach.

Effective governance begins with a rules engine that defines patient eligibility using EHR data (Epic, Cerner) and approved clinical guidelines. This engine, integrated with a segmentation AI agent, processes prior imaging to calculate a risk-adjusted outreach priority score. All patient selections and risk scores are logged to an audit database before any communication is triggered, ensuring decisions are traceable to source data and configurable business rules for compliance and performance review.

A phased rollout is critical. Start with a single screening type (e.g., lung cancer) and a controlled patient cohort. Initial phases should route all generated outreach through a human-in-the-loop approval queue within the CRM (Salespace, Health Cloud) before sending. As confidence builds, automate the approval gate for high-confidence segments while maintaining manual review for edge cases. Continuous monitoring of response rates, opt-outs, and subsequent screening appointments via integration hooks provides the feedback loop to tune rules and messaging, scaling the program across modalities and populations.