Cross-Community Patient Discovery
E738001
Cross-Community Patient Discovery is an IHE profile that enables healthcare organizations in different communities or domains to locate and match patient records across disparate health information systems.
All labels observed (5)
How this entity was disambiguated
This entity first appeared as the object of triple T8521827 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cross-Community Patient Discovery Context triple: [IHE XCPD, fullName, Cross-Community Patient Discovery]
-
A.
Centre for Collaboration with Digital Health Networks
The Centre for Collaboration with Digital Health Networks is a unit within the Norwegian Institute of Public Health that focuses on coordinating and advancing partnerships and initiatives in digital health.
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B.
Centre for Collaboration with Registry Networks
The Centre for Collaboration with Registry Networks is a specialized unit within Norway’s public health system that coordinates and advances the use of health registries for research, surveillance, and policy development.
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C.
Centre for Collaboration with Learning Health Systems Networks
The Centre for Collaboration with Learning Health Systems Networks is a unit within the Norwegian Institute of Public Health that focuses on partnering with health system networks to continuously generate, share, and apply evidence for improving healthcare quality and outcomes.
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D.
Centre for Collaboration with Systems for Health Information Networks
The Centre for Collaboration with Systems for Health Information Networks is a specialized unit within Norway’s public health sector that focuses on coordinating and improving digital health information systems and data sharing for public health purposes.
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E.
Jameel Clinic for Machine Learning in Health
The Jameel Clinic for Machine Learning in Health is an MIT research center focused on applying artificial intelligence and machine learning to transform disease prevention, diagnosis, and treatment.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Cross-Community Patient Discovery Target entity description: Cross-Community Patient Discovery is an IHE profile that enables healthcare organizations in different communities or domains to locate and match patient records across disparate health information systems.
-
A.
Centre for Collaboration with Digital Health Networks
The Centre for Collaboration with Digital Health Networks is a unit within the Norwegian Institute of Public Health that focuses on coordinating and advancing partnerships and initiatives in digital health.
-
B.
Centre for Collaboration with Registry Networks
The Centre for Collaboration with Registry Networks is a specialized unit within Norway’s public health system that coordinates and advances the use of health registries for research, surveillance, and policy development.
-
C.
Centre for Collaboration with Learning Health Systems Networks
The Centre for Collaboration with Learning Health Systems Networks is a unit within the Norwegian Institute of Public Health that focuses on partnering with health system networks to continuously generate, share, and apply evidence for improving healthcare quality and outcomes.
-
D.
Centre for Collaboration with Systems for Health Information Networks
The Centre for Collaboration with Systems for Health Information Networks is a specialized unit within Norway’s public health sector that focuses on coordinating and improving digital health information systems and data sharing for public health purposes.
-
E.
Jameel Clinic for Machine Learning in Health
The Jameel Clinic for Machine Learning in Health is an MIT research center focused on applying artificial intelligence and machine learning to transform disease prevention, diagnosis, and treatment.
- F. None of above. chosen
Statements (50)
| Predicate | Object |
|---|---|
| instanceOf |
IHE profile
ⓘ
health information exchange profile ⓘ |
| addressesChallenge |
distributed patient records
ⓘ
fragmented patient identities ⓘ |
| aimsTo |
enable safe patient matching
ⓘ
improve continuity of care across communities ⓘ |
| appliesTo |
cross-border eHealth projects
ⓘ
national health information networks ⓘ regional health information organizations ⓘ |
| belongsToDomain | IT Infrastructure ⓘ |
| constrains |
message content for patient discovery queries
ⓘ
message content for patient discovery responses ⓘ |
| definedBy | Integrating the Healthcare Enterprise ⓘ |
| definesActor |
Initiating Gateway
ⓘ
Responding Gateway ⓘ |
| definesTransaction |
Cross Gateway Patient Discovery
ⓘ
linked to:
Cross-Community Patient Discovery
|
| enables |
cross-border health information exchange
ⓘ
federated patient discovery ⓘ location of patient records in external communities ⓘ matching of patient identities across disparate systems ⓘ |
| focusesOn |
interoperability between health information exchanges
ⓘ
patient identity management across communities ⓘ |
| hasAbbreviation | XCPD ⓘ |
| hasAcronym | IHE XCPD ⓘ |
| hasSpecificationType | technical framework ⓘ |
| hasTransactionId | ITI-55 ⓘ |
| hasVersioning | supplements and revisions over time ⓘ |
| isRelatedTo |
Cross-Community Access
ⓘ
linked to:
IHE Cross-Community Access
PIX ⓘ Patient Identifier Cross-Referencing ⓘ XCA ⓘ |
| isUsedFor |
cross-community ePrescription services
ⓘ
cross-community patient summary exchange ⓘ |
| isUsedIn | eHealth Digital Service Infrastructure in Europe ⓘ |
| publishedBy | IHE IT Infrastructure Technical Committee ⓘ |
| requires |
governance agreements between domains
ⓘ
patient consent policies defined by communities ⓘ trust framework between communities ⓘ |
| supports |
query by patient demographics
ⓘ
query by patient identifiers ⓘ |
| supportsArchitecture |
federated health information exchange
ⓘ
hub-and-spoke health information exchange ⓘ |
| supportsUseCase |
cross-community patient identification
ⓘ
patient demographic query across domains ⓘ patient record location across communities ⓘ |
| usesStandard |
HL7 v3
ⓘ
linked to:
HL7 v3 Reference Information Model
SAML ⓘ SOAP web services ⓘ WS-Addressing ⓘ X.509 certificates ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: Cross-Community Patient Discovery Description of subject: Cross-Community Patient Discovery is an IHE profile that enables healthcare organizations in different communities or domains to locate and match patient records across disparate health information systems.
Referenced by (9)
Full triples — surface form annotated when it differs from this entity's canonical label.
linked to: Cross-Community Patient Discovery
linked to: Cross-Community Patient Discovery
linked to: Cross-Community Patient Discovery
linked to: Cross-Community Patient Discovery
linked to: Cross-Community Patient Discovery