Health Information Exchange Solutions

Databricks is becoming a wrecking ball for data silos. About time I upgraded this diagram, because last month Databricks Clean Rooms became Generally Available. This introduces yet another way to share data and to collaborate on data projects with other data practitioners. The best part: you can bring in data from non Databricks (and even other public cloud) sources (like Synapse, Snowflake, Redshift or BigQuery) as long as these data sources are governed by a Unity Catalog. The whole idea is that enterprises can now share and work together with other enterprises in a secure and controlled environment. This makes it possible to collaborate on sensitive data projects without any risk of exposing sensitive information. Clean Rooms runs on Delta Sharing, which is become quite the standard I must say. Note: There are loads of loose ends in the diagram, but the idea is that we now have a way of collaborating on data cross-organization, rather than just sharing or integrating data via Spark Connect, Unity Catalog or Delta Sharing.

🗞️ Just out! Latest from our NATO Strategic Communications Centre of Excellence ! “Democratising Data Integration” 🔹Examines the need for standardised data integration and communication protocols in NATO’s strategic information environment. 🔹 Core argument : while advanced data processing tools exist, the lack of standardised integration protocols limits efficiency, security, and rapid decision-making. 🔹Highlights the challenges of fragmented data systems, interoperability issues, and inconsistent data-sharing methodologies across allied organisations. Key Challenges 1. Metadata Standardisation – Inconsistencies in metadata structures lead to misinterpretations and operational inefficiencies. 2. Security Classifications – Differing classification methods create access restrictions, limiting data-sharing effectiveness. 3. Institutional Divergence – NATO allies use various data-sharing protocols, impeding interoperability. 4. Technical Expertise Gaps – The shortage of skilled personnel slows the adoption of modern integration frameworks. 5. Resource Constraints – Budgetary limitations restrict the transition to scalable and secure data systems. 6. Privacy and Compliance Issues – Conflicting regulations (e.g., GDPR) create legal and operational barriers. Proposed Solutions 🔹The report proposes adopting standardised communication protocols to ensure seamless interoperability. Frameworks like Federated Mission Networking (FMN) and VAULTIS are highlighted as potential models for structured data sharing. AI-driven solutions, automated classification systems, and improved governance mechanisms are recommended to enhance operational efficiency. Standardisation would lead to: 🔹Improved Strategic Communications – Faster, more reliable data-driven decision-making. 🔹Operational Efficiency – Reduced manual processing, better crisis response. 🔹Cost-Effectiveness – Lower integration costs through streamlined interoperability.

🧬 We talk about “health data” as if it’s one thing, but it’s really hundreds of incompatible languages trying (and failing) to talk to each other. Every layer speaks a different dialect: • EHRs: HL7 v2, CDA, FHIR • Claims: X12 837, UB-04, CMS-1500 • Labs: LOINC, SNOMED CT • Devices: DICOM, IEEE 11073 • Genomics: VCF, FASTQ, BAM Each was built for a single purpose, not interoperability. The result? 🚑 A patient’s data is scattered across 40+ systems, each with its own schema, timestamps, and access controls. But things are shifting. Newer models are moving beyond formats to: • Graph-based data structures • Semantic layers • Federated architectures These approaches preserve context, not just content, across systems. FHIR paved the road. But the next frontier is semantic interoperability. That’s not just data exchange; it’s data understanding. 🧠 The future of healthcare intelligence isn’t in collecting more data, it’s in connecting meaning. #HealthTech #DataInteroperability #FHIR #HealthcareAI #KnowledgeGraphs #SemanticWeb

FHIR is not a silver bullet that solves all interoperability problems. Throwing money at a big FHIR project that exposes or consumes data via FHIR APIs does not mean you can tick the “Interoperability” checkbox. Here are some examples of how systems using FHIR can look interoperable without truly being interoperable. 1. “FHIR compliant” is not the same as good data - Data can pass FHIR validation and still be incomplete, inconsistent, or misleading. 2. Context can be lost in transmission - Observations recorded without Encounters, intent or circumstances. 3. FHIR validates structure, not plausibility - A perfectly valid 5 year old patient: male, married and pregnant. 4. Coded data can still be bad data - Custom codes when bindings are loose. Text values in place of codes. 5. Bad data can be shared as easily as good data - FHIR enables all data to move efficiently (bad data as well as good data). When two systems speak to each other using FHIR for the first time, there’s often celebration. “Data is flowing. Everything is working!” But a key part of Interoperability is that data should be usable by other systems. If another system can read your FHIR data but is unable to confidently use it, then you are NOT interoperable, no matter how valid and compliant your FHIR resources are. When data flows but meaning doesn’t, interoperability is an illusion. ~~~ Ways to work with me: https://lnkd.in/eWwXNk_U

When I say, ‘See you at 7’, do I mean 7 AM or 7 PM? ⏰ This is what we call, an interoperability problem - the inability for systems to exchange data and understand it in the same way. Why is interoperability in healthcare so hard? Because it’s not just a tech issue. It’s a stack of challenges And the hardest part isn’t connection, it's understanding. Let’s break this down. 1️⃣ Technical Interoperability - can systems connect and exchange data? Sounds simple, until: 🔸One system uses CSV, another wants XML 🔸Dates are DD/MM/YYYY vs MM/DD/YYYY 🔸Fields don’t match or exist Without standard formats, even basic connections break. Challenging - yes. But ironically, the easiest layer to fix. (Most teams stop here. That’s the issue.) 2️⃣ Semantic Interoperability - Can systems understand the data? Take “discharge date” as an example: 🔸One system uses the paperwork date 🔸Another, the bed exit time 🔸A third, the billing date Same label, different meanings. Now try running a report across all three. This is where projects quietly fail. Semantics needs shared meaning, clinical context, and governance. (And that’s just admin data, imagine lab values, diagnoses, or clinical notes. Get it wrong and it’s not just inefficiency, it’s a safety issue!) 3️⃣ Workflow Interoperability - do systems fit real care delivery? 🔸A patient sees a doctor in the morning, does a lab test in the afternoon 🔸Lab results are ready but not visible till the next day 🔸Why? The EHR and lab system don’t sync in real time, and no one flagged it. Digital isn’t fast if the workflow stays broken. 4️⃣ Organizational Interoperability - do institutions even want to collaborate? 🔸Hospitals, clinics, insurers, labs etc. have different systems, incentives, and vendors 🔸Even if tech and semantics align, nothing moves without shared ownership The real question isn’t “Can systems talk?” It’s “Do they understand each other and act together?” And more importantly - who’s responsible for making that happen? Because in healthcare, everyone is in charge, yet no one really is. Let’s stop treating interoperability like a checkbox and start treating it as a system-wide commitment: to shared meaning, coordinated action, and patient-centered design. What’s one interoperability headache you’ve seen that should’ve been solved by now? #Interoperability #SemanticStandards #SystemThinking #HealthData 💡This post is part of 'Rethinking Digital Health Innovation' (RDHI), empowering professionals to transform digital health beyond IT and AI myths. 💡The ongoing series and additional resources are available at http://www.enabler.xyz 💡Repost if this message resonates with you!

Perplexity’s AI search meets b.well Connected Health’s platform to deliver personalised, data grounded health answers >> 🔘Perplexity and b.well are linking AI search directly to individual health records, moving from generic health queries to answers grounded in a person’s actual medical history 🔘The model allows users to connect data across providers, health systems and insurers, turning fragmented records into a more unified and usable health view 🔘This aims to shift AI from an information tool to a contextual layer that can interpret labs, medications and conditions in a way that is specific to the individual 🔘The approach is permission based, with users controlling access to their data, highlighting that trust and reversibility are becoming core to digital health design 🔘Alongside recent developments from Microsoft, Anthropic and OpenAI, the direction of travel is clear, value is moving away from the model alone and towards the combination of AI with high quality, longitudinal patient data 💬Health AI is shifting from asking better questions to getting answers grounded in your own biology #digitalhealth #AI

Healthcare’s next interoperability challenge is not simply moving more data. It is establishing confidence in who is requesting the data, which organization they represent, and what authority they have to act. Scott Stuewe FACHDM, President and CEO of DirectTrust, highlights an increasingly important reality: identity alone is not enough. As healthcare moves toward API-based exchange across TEFCA, payer networks, providers, applications, and consumer-directed services, we also need trusted organizational identity and verifiable delegated authority. This is where healthcare’s digital trust infrastructure must evolve. We need scalable approaches that can: 🔹 Reliably identify the legal entity behind a transaction 🔹 Connect individuals, applications, and endpoints to the organizations they represent 🔹 Communicate delegated authority and the context of an exchange 🔹 Reduce duplicative, manual onboarding across networks 🔹 Support trust decisions across organizational and jurisdictional boundaries This challenge closely aligns with the work underway through HL7 FAST Identity and our collaboration with DirectTrust, the CARIN Alliance, GLEIF, credential service providers, and other industry partners. Our collective goal is to help establish standards-based identity and trust frameworks that can support secure FHIR exchange at national—and ultimately global—scale. Interoperability cannot scale through connectivity alone. It requires an infrastructure through which identity, authority, purpose, consent, and accountability can be consistently understood and verified. Excellent perspective from Scott and an important foundation for the next phase of trusted healthcare data exchange. https://lnkd.in/g8a_228r #HealthcareInteroperability #DigitalIdentity #FHIR #HL7FAST #DirectTrust #TEFCA #HealthIT #DigitalTrust #DataExchange #PatientAccess

🚀 Delta Sharing: The Open Protocol for Secure Data Exchange Traditionally, data sharing involved providing static CSV/Parquet file dumps based on ad-hoc requests, requiring data engineers to create extracts or build complex ETL pipelines. By the time data reached recipients, it was often outdated. Additionally, moving data across organizational boundaries increased security risks and required manual auditing as well. Delta Sharing, an open protocol, solves these challenges by enabling direct, real-time data exchange while ensuring security and governance. 🔍 What is Delta Sharing? Delta Sharing is an open-source protocol that allows data providers to securely share live data from their data lake or lakehouse with any recipient, regardless of the computing platform they use. It is designed to work with Delta Lake, but it also supports other formats like Apache Parquet. 🔧 What Problems Does Delta Sharing Solve? ✅ Eliminates Data Copies – Consumers can query shared data without duplicating or exporting it into another system. ✅ Interoperability – Enables cross-platform sharing across different cloud and analytics services, including Databricks, Apache Spark, Pandas, and others. ✅ Real-time & Secure Access – Uses fine-grained access control to ensure only authorized users can access the latest version of shared data. ✅ Simplified Data Collaboration – Reduces the need for custom APIs, FTP transfers, or complex ETL workflows when sharing data with external partners. 🛠 Key Components in a Delta Sharing Scenario - Provider (Data Owner) – The entity sharing the data. - Delta Sharing Server – Handles authentication and access control. - Recipient (Data Consumer) – The entity accessing the shared data, which can be a data warehouse, a machine learning model, or a BI tool. - Storage Backend – Typically an object store (AWS S3, Azure Blob, Google Cloud Storage, MinIO) where the data resides. 📌 Common Use Cases for Delta Sharing 💡 Inter-company Data Exchange – Share supply chain, financial, or operational data with partners securely. 📊 Federated Analytics – Analysts can query live shared datasets without moving them into their own data warehouse. 🤖 Machine Learning & AI – Data scientists can directly access fresh, live data for model training without worrying about outdated extracts. ⚡ Data Monetization – Organizations can offer secure access to valuable datasets as a service without needing data pipelines. Delta Sharing + Unity Catalog Delta Sharing and Unity Catalog work together to enable secure, scalable, and governed data sharing across organizations. While Delta Sharing provides the protocol for sharing live data with external consumers, Unity Catalog acts as the central governance layer, ensuring fine-grained access control, auditing, and security compliance. I will write about this integration in the future. #deltasharing #datagovernance #datasharing

How do you move clinical data for 8 million people across hundreds of hospitals? This question came up while we were working with the Catalan government on a national-scale openEHR deployment. In 2023, Catalonia launched Spain’s first federated openEHR platform. The Catalan Health Service (CatSalut) wasn’t just procuring another EHR. The goal was to rethink how clinical data could be shared across an entire region while preserving local autonomy. Hospital care in Catalonia spans hundreds of providers and nearly 30 different hospital EMR systems. A single central system would have struggled with both scale and governance. Instead, Catalonia chose a federated model built on openEHR. Medblocks was part of the consortium that delivered this platform, alongside vitagroup and IBM. The core challenge was straightforward to describe, but hard to implement: When a patient moves from one hospital to another, their clinical data should move with them - safely, quickly, and without losing context. What we built: We helped design and implement a federated architecture where local hospital CDRs connect to a central CDR, using openEHR as the shared clinical model. This involved: 1. Event-driven synchronization between local and central repositories 2. Explicit handling of persistent compositions like medication lists 3. Conflict detection and manual resolution instead of unsafe auto-merging 4. Shared templates and AQLs as the foundation for reliable data exchange We’ve published a Youtube video walking you through the architecture, the trade-offs, and the lessons learned from operating at population scale. You can check it out in the comment section.

🚀 The Integration Applications Landscape in U.S. Healthcare Interoperability is the backbone of digital health. Every claim submitted, lab result transmitted, eligibility verified, prescription shared, or FHIR API invoked runs on an integration layer. Here’s a clear, practical breakdown of the major integration application categories used across payers, providers, HIEs, and digital health companies and what each one is best suited for. 🔹 1.Interface Engines (HL7 v2, On-Prem Integrations) These tools power classic ADT, ORU, ORM, SIU, and lab interfaces within hospitals. Examples: Mirth/NextGen Connect, Cloverleaf, Rhapsody, Corepoint Use Cases: • Real-time clinical messaging • EHR ↔ Lab ↔ Radiology interfaces • High-volume, low-latency HL7 pipelines 🔹 2.FHIR & API Integration Platforms Built for modern digital health and app ecosystems. Examples: Redox, MuleSoft (Healthcare Accelerator), Smile CDR, Google Apigee Use Cases: • Patient Access APIs (CMS-9115-F) • App integrations across multiple EHRs • API gateway + transformation + developer onboarding 🔹 3.Managed Cloud Healthcare Data Platforms HIPAA-compliant FHIR/HL7/DICOM storage + advanced analytics. Examples: Azure Health Data Services, Google Healthcare API, AWS HealthLake Use Cases: • Unified clinical + claims data platforms • Analytics, AI/ML, LLM-based insights • Multimodal storage (FHIR, DICOM, HL7v2) 🔹 4.Enterprise Integration & Orchestration Suites Combine messaging, workflows, and rules engines. Examples: InterSystems IRIS/Ensemble, IBM Integration Bus Use Cases: • Complex payer/provider workflows • Eligibility → Benefits → Claims orchestration • Low-code integration with embedded business logic 🔹 5. Administrative & EDI Integration Engines Handle HIPAA X12 transactions across payers and clearinghouses. Examples: Edifecs, Change Healthcare, Optum Intelligent EDI, Availity Use Cases: • 837 claim intake, • 270/271 eligibility • 835 ERA/EDI remittance • Provider credentialing/enrollment feeds 🔹 6. HIE /Nationwide Connectivity Networks Enable large-scale data exchange and care coordination. Examples: Surescripts, Carequality, CommonWell, eHealth Exchange Use Cases: • Medication history • Clinical document exchange • Cross-organization interoperability 🔹 7. ETL & Healthcare Data Engineering Tools Focus on analytics, actuarial use cases, and warehouse pipelines. Examples: Talend, Informatica, Databricks, Snowflake HC solutions Use Cases: • Claims data transformation • Risk, quality, HEDIS, actuarial models • Clinical + administrative data reconciliation 💡 Choosing the Right Tool • Hospital operations → Interface Engines • Digital health products → FHIR/API Platforms • Payer analytics → ETL + Cloud Data Platforms • Enterprise modernization → Orchestration Suites • Claims & eligibility workflows → EDI Engines #HealthcareIntegration #Interoperability #FHIR #HL7 #EDI #DigitalHealth #HealthcareIT #HealthTech #PayerProvider #APIs #CloudHealth #HealthcareData #USHealthcare