Infrastructure for Cancer Screening Program Management

Cancer screening program management requires explicitly documented eligibility criteria for each screening type—age windows, risk factor thresholds triggering earlier or more frequent screening, and payer quality measure specifications (HEDIS, UDS). When mammography eligibility is '40-74 annually or 50-74 biennially depending on payer' and that nuance lives only in the quality department's institutional knowledge, the AI cannot correctly identify which patients are due and under which protocol. Screening protocols must be current, findable, and linked to specific population definitions.

Screening program management requires systematic capture of last screening exam dates, results, risk factors (family history, smoking pack-years), patient barriers (transportation, language), and outreach attempt outcomes. Template-driven capture of screening history and barrier assessments ensures the AI can correctly calculate when each patient is next due and personalize outreach. Outreach attempt outcomes—completed, declined, unreachable—must be logged to prevent duplicate contacts and track campaign effectiveness.

Screening program management requires consistent schema: patient demographics fields, screening type codes, last screening date, result category (normal, abnormal, pending), risk factor indicators, barrier type codes, and outreach preference fields. Without standardized structure, the AI cannot systematically identify which patients are due for which screening under which protocol. Payer quality measure requirements must be structurally mapped to patient eligibility criteria to enable compliance tracking by measure.

Cancer screening program management must access EHR for patient demographics and clinical history, scheduling systems for appointment availability, payer quality measure specifications, and communication platforms for outreach. API access to the EHR enables automated identification of eligible patients without manual cohort extraction. For navigation workflow triggers on positive results, the system must access EHR result data and route alerts to the appropriate navigator. External facility screening data remains a gap per HIE limitations in the baseline.

Cancer screening eligibility criteria update when USPSTF issues guideline revisions, when CMS updates quality measure specifications, and when individual payer contracts change HEDIS measure thresholds. The AI's patient identification logic must reflect current guidelines—event-triggered updates when USPSTF revises lung cancer screening age criteria ensure the system immediately expands the eligible population rather than waiting for quarterly review. Payer-specific quality measure specifications require contract-change-triggered updates.

Cancer screening program management requires integration between EHR (patient demographics, clinical history, results), scheduling systems (appointment booking), communication platforms (outreach campaigns), and quality measure reporting. The baseline confirms EHR integration and UM software connectivity. API-based connections enable the screening AI to push patient lists to outreach platforms, trigger navigator workflows on positive results, and pull appointment completion confirmations back for quality measure tracking. Community resource and transportation integration remains limited per baseline HIE constraints.