💡 Healthcare AI Agent Use Cases

AI agent connects to EHR (Epic, Cerner) via FHIR R4 API, retrieves patient demographics, medical history, and previous mammogram records. Uses SMART on FHIR for secure authentication.

Applies CQL (Clinical Quality Language) rules based on USPSTF guidelines: age 50-74, female gender, no mammogram in past 27 months. Processes structured FHIR data and unstructured clinical notes using NLP.

Generates approved/denied/needs-more-info decision with detailed rationale citing specific USPSTF criteria met or not met. Creates FHIR-compliant decision bundle with evidence and provenance.

Communicates decision to payer via A2A protocol with complete evidence package. Handles prior authorization if required, tracks status, and manages appeals automatically.

Updates EHR with decision, schedules screening appointment if approved, sends patient portal notification, and coordinates with imaging center for scheduling.

Monitors for completed screening, processes results, updates patient record, schedules follow-up if needed, and tracks outcomes for quality measures.

AI agent monitors EHR for prescriptions/procedures requiring prior auth. Auto-extracts patient data, diagnosis codes, clinical notes, supporting documentation. Submits complete request package to payer via X12 278 or proprietary API.

Applies evidence-based medical necessity criteria using CQL rules and ML models trained on millions of approvals. Analyzes patient history, medication trials, failed treatments, contraindications, and payer-specific policies.

Identifies missing documentation, auto-generates request for records, queries EHR for additional data, synthesizes clinical notes, creates evidence package with proper citations and references.

Processes payer decision, updates EHR with authorization number, communicates to provider and patient, schedules procedure if approved, auto-files appeal if denied with complete evidence package and precedent cases.

For denials, auto-generates appeal with additional clinical evidence, peer-reviewed literature, similar approved cases, cost-effectiveness data. Escalates to human review only if ML confidence < 85%.

Parses ClinicalTrials.gov protocols, extracts inclusion/exclusion criteria, converts to machine-readable CQL expressions, maintains database of 400K+ active trials with real-time updates.

Continuously scans EHR population (FHIR $everything), identifies potential candidates based on diagnoses, treatments, demographics. Uses NLP to extract information from unstructured clinical notes, pathology reports, genomic data.

Applies complex multi-criteria matching with ML-based similarity scoring. Considers prior treatment lines, biomarkers, comorbidities, geographic proximity, patient preferences. Ranks trials by match quality and feasibility.

Notifies treating physician with trial details and evidence of eligibility. Sends patient-friendly trial summaries via patient portal. Coordinates with research site for screening appointments. Tracks interest and enrollment status.

Monitors screening visits, consent process, enrollment confirmation. Updates trial database, provides real-time enrollment metrics, predicts trial completion dates, identifies enrollment bottlenecks for optimization.