Learn how to design closed-loop patient-referral platforms from scratch, covering workflow states, application boundaries, integration patterns, analytics, AI safety, and regional governance across EHR, MPI, RIS, PACS, HL7 v2, FHIR, DICOM, and IHE profiles.
Build the referral mental model: workflow states, source systems, accessioning, and closed-loop completion.
Design the referral platform itself: bounded contexts, identifiers, state ownership, and contract surfaces from intake through closure.
Understand who originates referrals, what belongs in the order, and where accountability passes between roles.
Validate identity, codes, clinical question, and supporting context before routing, triage, and automation continue.
Separate urgency, appropriateness, and protocol planning so a raw request becomes an executable service plan.
Match modality, staff, prep tasks, and patient communications to real operational capacity.
Trace arrival, modality execution, reporting, and results distribution back to the original requester.
Compare HL7 v2, FHIR, DICOM, and IHE patterns for tracing the same referral across messages, workflow documents, bookings, and results.
Connect referral platforms to identity, document sharing, registries, repositories, portals, and other upstream or downstream systems.
Apply identity, privacy, audit, and regional operating rules, including New Zealand referral standards and CRR.
Turn referral states into measurable operational signals for backlog control, turnaround tracking, and service-improvement dashboards.
Apply AI to referral operations through reviewable suggestions, explicit provenance, and safety controls instead of hidden queue mutation.
Design event-driven automation, exception queues, observability, and controlled rollout for referral operations.
Test your knowledge across all learning areas with our comprehensive assessment.