Approaching

MPI integration in 2026 splits along a clear architectural line. One side exposes the MPI through FHIR Patient resource endpoints and consumes inbound demographic data as FHIR Bundles. The other side speaks HL7 v2 ADT messages over MLLP, th

Probabilistic and referential matching are the two approaches that dominate the long tail of patient matching once the obvious deterministic cases have been handled. They solve overlapping but distinct problems, and the choice between them

Running an MPI on-prem comes with operational overhead that many 2026 healthcare IT teams would rather not absorb: patching, scaling, backup, monitoring, capacity planning. Cloud-hosted MPI services move that responsibility to the vendor, i

Health information exchanges depend on patient matching to be useful. An HIE that cannot reliably link records from member hospitals delivers a fragmented view that no clinician trusts, which makes the participating organizations stop contr

The patient identifier is the unit of work that an MPI emits. In a FHIR integration, that identifier flows into the Patient resource's identifier array, gets referenced by every clinical resource downstream, and ultimately decides whether a

Telehealth platforms face a different MPI problem than hospital networks. Patient registration arrives through self-service portals, demographic data is patient-entered rather than staff-entered, and the deployment usually has no physical f

Duplicate patient records are the steady-state problem an MPI exists to solve. Every healthcare organization has them, every new EHR rollout produces more, and every reconciliation effort surfaces a new round of edge cases. The four algorit

Cross-state health information exchanges face the hardest patient-matching problem in healthcare. The same patient can appear with a different address in two states, a different name spelling on a marriage record, and a different phone numb