饾悡饾悺饾悶 饾煇饾煄饾煇饾煋 饾悆饾悋饾悎饾悕 饾悅饾惃饾惂饾惂饾悶饾悳饾惌饾悮饾惌饾悺饾惃饾惂 饾悽饾惂 饾悘饾悽饾悳饾惌饾惍饾惈饾悶饾惉 馃摳
From testing tracks to technical workshops, standards sessions to hands-on interoperability challenges, the energy at this year鈥檚 DHIN Connectathon was simply unmatched.
For three days, experts, innovators, students, regulators, global partners, and digital health builders came together to validate real-world use cases using FHIR, IPS v2, ICD-11, SNOMED CT, and more.
Behind every health record is a person who deserves both safety and privacy.
Good identifier strategy makes sure patients are matched correctly and their rights are protected.
At the 2025 DHIN Connectathon, we鈥檒l show how national IDs, program IDs, and pseudonymous keys come together to make this possible. 馃挕
#DigitalID #HealthData #PublicHealth #Interoperability #Innovation #Hackathon #DigitalHealth #DHINConnectathon2025
Without shared registries, interoperability collapses into silos.
At the 2025 DHIN Connectathon, we鈥檙e putting shared registries to the test: proving how they anchor trust, enable safer care, and power real-world impact.
#HealthData #Interoperability #DigitalHealth
Policies don鈥檛 save lives until they鈥檙e put into practice.
At the 2025 DHIN Connectathon, we鈥檙e applying SMART Guidelines to transform global standards into daily tools and tools into measurable health outcomes for all.
#DigitalHealth #GlobalHealth #HealthEquity
DHIN endorses the Principles of Digital Development
The Digital Health Interoperability Network (DHIN) is proud to officially endorse the聽Principles of Digital Development - a globally recognised set of living guidelines that put people at the centre of digital transformation.
Together with our partners, we will continue to build digital public infrastructure that strengthens health systems, empowers communities, and drives equitable access to care.
馃敆 Learn more about the Principles of Digital Development here: lnkd.in/dzQeJgb
AI-Powered HIEs: Transforming Data Pipes into Intelligence Engines
Traditional HIEs often act as passive conduits, passing clinical data as-is without interpretation or standardization. The result: disparate formats, inconsistent vocabularies, and fragmented insights.




















