Integrating Healthcare Services

This well-intentioned claim has killed more provider-focused healthcare startups than any other: "We'll integrate with any EHR!" The reality of healthcare integration: Epic integration isn't just technical – It's political. Without App Orchard certification, you're facing 6+ months of custom work per client. With it, you still need local IT champions and competing priorities. Cerner's domain model creates fundamentally different data structures across implementations. What works at Intermountain won't work at Ascension without significant customization. Meditech/CPSI/Athena customers often lack the technical resources to manage complex integrations – regardless of what your sales team promises. HL7 isn't a standard – it's a framework. Each organization implements it differently, with custom segments, Z-segments, and proprietary extensions. FHIR readiness varies wildly – Most health systems have implemented just enough to meet Meaningful Use requirements, not enough to support your full workflow. The operational blindspots: Integration governance means your solution competes against 50+ other projects. Interface engine capacity is a finite resource you didn't budget for. Testing environments that don't match production. Downtime procedures you didn't design for. This isn't just a technical challenge. It's a market architecture problem that must be solved pre-sale. The most successful healthcare technology companies don't have the "best" integration – they have the most pragmatic implementation strategy that aligns with how health systems actually work. If your deals are stalling during implementation, let's diagnose the real issues. #healthcareintegration #implementationstrategy #ehrimplementation

🧬 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

What can #syntheticdata do to transform healthcare? Sharing real patient data – though vital for biomedical research – is often fraught. Synthetic data, generated by #privacypreserving models, offers promising solutions.   But how effective is synthetic data in practice, especially in the critical area of clinical model development?   In our Scientific Reports paper, we built synthetic versions of one of the world’s richest and most complex biomedical datasets – the UK Biobank – to explore the vast potential of synthetic data for privacy-preserving clinical risk prediction.   #Privacy: We compared multiple privacy-preserving synthetic data generators - synthetic data can replicate complex real data patterns without exposing sensitive patient information.    #ModelDevelopment: Synthetic data proved to be a viable substitute throughout the medical prognostic modelling pipeline. We were able to develop accurate lung cancer prognostic models without ever accessing the real patient data.   #SyntheticDataDeployment: Highlighting different data release approaches, we showed how synthetic biobank data could be integrated into the healthcare system and how it could accelerate research.    Our paper is available here: https://lnkd.in/ecuv8Svu Zhaozhi Q. / Tom Callender / Bogdan Cebere / Sam Janes / Neal Navani

7 Reasons We Should Adopt OpenEHR In healthcare, data isn’t actually just data, it’s the difference between good decisions and bad ones, between life and death. Yet, most healthcare IT systems today treat data like a locked filing cabinet: siloed, rigid, and nearly impossible to share. That’s where OpenEHR comes in. Here’s why it’s time to adopt it: 1. Interoperability That Actually Works Healthcare IT is infamous for fragmented systems that don’t talk to each other. OpenEHR creates a unified data layer that makes it easy to share patient records across different systems, vendors, and countries. No more custom integrations or clunky workarounds. 2. Data Longevity (Future-Proofing Healthcare) Most health records are locked into proprietary systems that become obsolete over time. OpenEHR separates data from applications, ensuring that information remains accessible even as software evolves. Think of it as PDF for health records, standardised, portable, and always readable. 3. Lower IT Costs (Goodbye Vendor Lock-In) Traditional electronic health records (EHRs) trap hospitals in expensive, long-term contracts. OpenEHR’s open standards allow healthcare providers to choose the best tools without being forced into one vendor’s ecosystem. The result? Lower costs, better competition, and faster innovation. 4. Clinical Engagement in IT Decisions Most EHRs are designed by IT teams, not clinicians, which is why they often frustrate doctors and nurses. OpenEHR flips this by enabling clinicians to define their own data models (archetypes), ensuring that health records align with real-world medical needs. 5. Real-Time Data for AI & Analytics AI in healthcare is only as good as the data it’s trained on. OpenEHR structures and normalizes health data, making it perfect for machine learning, predictive analytics, and real-time decision support. It’s the foundation for next-gen digital health. 6. Scalability for National Health Systems OpenEHR isn’t just for hospitals, it’s built for national and regional healthcare infrastructures. Countries like Finland, Norway, Slovenia and Catalunya are already using OpenEHR to standardise and scale their digital health strategies. 7. Empowering Patients with Data Ownership Patients are tired of feeling like their own health data is hidden from them. OpenEHR makes it easier to give patients control, supporting patient-facing applications, wearables, and personal health records. Healthcare needs data liquidity, not digital silos. OpenEHR is the best shot we have at a scalable, vendor-neutral, patient-centric health IT system. It’s not just a technology choice, it’s a long-term investment in the future of healthcare. So, the real question is: Why haven’t we adopted it yet?

A network on paper isn’t access. A provider listing isn’t care. A wait time isn’t an excuse. The recent WSJ investigation ( 🔗 in comment) laid bare a troubling reality that I remember quite clearly in my FQHC clinical experiences: in many states, Medicaid provider networks appear “adequate” on paper but collapse in practice—filled with clinicians who don’t accept Medicaid patients, aren’t taking new patients, or have no timely appointments available. In my experience overseeing VA Community Care, particularly following the MISSION Act of 2018, these issues are deeply familiar. That law expanded veterans’ access to community providers when VA wait times or distance created barriers—and it also exposed how network adequacy failures often reflect geographic gaps and unacceptable wait times. Medicaid beneficiaries are now facing the same access inequities. A directory that promises care but delivers neither timeliness nor proximity is not a network—it’s a barrier. This is why joint accountability across the ecosystem is essential: ➡️ States must validate networks using real-world appointment availability, geographic access, and true Medicaid participation—not static provider lists. ➡️ Payors/Insurers must commit to transparency: verifying active participation, reporting wait-time data, and partnering with providers to strengthen capacity. ➡️ Providers must share timely capacity data, accept Medicaid patients at sustainable reimbursement levels, and participate in integrated care models that reduce fragmentation. A critical lever is telehealth, but not as a disconnected workaround. Telehealth must be a coordinated extension of integrated care, used strategically when geography, transportation barriers, or long wait times make in-person care unrealistic. When primary care, behavioral health, and pharmacy teams use shared information systems and telehealth as part of a unified workflow—not a separate system—patients finally get timely, coordinated access. We cannot continue calling networks “adequate” when patients wait months or must travel hours for essential care. True access requires shared responsibility, transparent data, and integrated models that meet people where they are—whether in person or virtually. #Medicaid #AccessToCare #NetworkAdequacy #MISSIONAct #HealthEquity #Telehealth #IntegratedCare #PrimaryCare #BehavioralHealth #Transparency #ValueBasedCare

AI is only as smart as its data. Bad data breaks everything. Good data builds the future. AI in healthcare is not magic. It is math, logic, and trust—stacked on a backbone of clean, connected data. Here’s the truth: • AI can’t fix broken data. • Automation fails if the data is a mess. • Connected care needs a solid data foundation. Think of data as the bones of a body. If the bones are weak, nothing stands. If the bones are strong, you can build muscle, move fast, and stay healthy. To build smarter AI and real connected care, start with these pillars: 1/ Data Quality:   Garbage in, garbage out.   Every record, every field, every update must be right.   No duplicates. No missing info. No errors.   Clean data is the first rule. 2/ Interoperability:   Systems must talk to each other.   Break down silos.   Use standards like HL7, FHIR, and APIs.   If your data can’t move, your care can’t connect. 3/ Privacy and Security:   Trust is everything.   Encrypt data.   Control access.   Follow HIPAA and GDPR.   Patients own their data—protect it. 4/ Governance:   Set the rules.   Who can see what?   Who can change what?   Audit trails, clear roles, and strong policies keep data safe and useful. 5/ Infrastructure Flexibility:   Cloud, on-prem, or hybrid—pick what fits.   Scale up as you grow.   Don’t get locked in.   Your data backbone must bend, not break. 6/ Continuous Improvement:   Data is never “done.”   Check, clean, and update all the time.   Train your team.   Make data quality a habit, not a project. When you get these right, you unlock: • Smarter automation • Real-time insights • Scalable AI that learns and adapts • Seamless patient care across systems The best AI in the world can’t save bad data. But with the right data backbone, you build care that connects, scales, and lasts. Start with better data. Build the future of healthcare—one clean record at a time.

My Friday post about the continued struggles of VBC struck a chord and led to an excellent discussion. Pointing out flaws and shortcoming is one thing (and something we have plenty of in healthcare). Ultimately, offering solutions is more helpful. CMMI's struggles suggest that value doesn’t reliably emerge from controlling disparate portions of the care continuum. Taken independently, reducing postop acute care, avoiding unnecessary procedures and imaging, and nudging primary care toward risk leads to some gains -- but the whole isn't always greater than the sum of its parts. These mechanisms help, but they're rarely transformative. Rather than stacking up small wins, we should be figuring out how to re-wire the system. That's how to get the big wins that have been elusive. True value emerges when the full care pathway is redesigned from end to end. Not to avoid or gatekeep care, but to incentivize detection and early intervention while making sure patients get the right care, earlier, more precisely, and with better feedback loops. Start with prevention, risk profiling, and early diagnosis. Build pathways that encourage—not delay—high-quality, efficient specialist care. Create small, focused centers of excellence that do a few things incredibly well. Then loop the patient back to a central hub for long-term maintenance and proactive management. Create a coordinated cycle of care that prevents problems, identifies them early when they arise, and delivers the highest quality, most specialized care when needed. Closing the loop means cycling patients back to the central healthcare hub of maintenance and prevention. This isn’t vertical integration for financial ends or carrot-and-stick value mechanisms. It’s ownership of the entire care process for better outcomes. Call it VBC or something else. Either way, its broad systemic redesign, not another confusing, cumbersome, bloated program. #healthcare #valuebasedcare #innovation

India spends more on healthcare each year but, the key question persists: are health outcomes improving at the same pace?   After six months of cross-sector collaboration, the report “𝗪𝗵𝗮𝘁 𝗪𝗲 𝗩𝗮𝗹𝘂𝗲 𝗶𝗻 𝗛𝗲𝗮𝗹𝘁𝗵: 𝗔 𝗖𝗼𝗮𝗹𝗶𝘁𝗶𝗼𝗻 𝗩𝗶𝘀𝗶𝗼𝗻 𝗳𝗼𝗿 𝗕𝗲𝘁𝘁𝗲𝗿 𝗖𝗮𝗿𝗲 𝗶𝗻 𝗜𝗻𝗱𝗶𝗮” has been released. It sets out a clear and strategic roadmap to help shift India’s health system from a volume-led approach to one centred on value and outcomes.   I am proud to have contributed to this unique coalition and to have shared perspectives from an insurer’s point of view. Months of structured, constructive dialogue and collaboration have helped reimagine the future roadmap of healthcare in India. With a ringside, three-tiered view of the ecosystem – across my organisation, the insurance sector, and the broader system – I was particularly invested in exploring avenues to strengthen health financing and drive sustainable outcomes.   Congratulations to Leapfrog to Value and its CEO Dr. Balkrishna Korgaonkar for spearheading this important effort and bringing together diverse voices across the healthcare ecosystem.   The coalition’s work identifies core systemic gaps such as fragmented care, limited transparency of outcomes, and financing structures that reward service volume over improved health. It also highlights promising bright spots across the system.   The initiative has resulted in four catalytic proposals: ✅ People’s Commission for Health Improvement – transparent benchmarking to strengthen accountability ✅ Primary Health Care Design Laboratory – prototyping integrated, outcome-focused care models ✅ Business Case for Quality, Safety & Patient Experience – aligning incentives with what truly matters to patients ✅ Coordinated Care Bundles – piloting bundled payments for NCDs and surgeries   The roadmap presents a practical agenda aimed at improving alignment, equity and measurable outcomes. It is an important step toward a more resilient and health-focused future for India.   You can download the strategy here: https://lnkd.in/gErbNiFV Bindu Ananth, Dr. N. Krishna Reddy, Ravi Vishwanath, Sarang Deo, Tejasvi Ravi, Vishnu Vasudev, Rubayat Khan, Dr. Balkrishna Korgaonkar and Chintan Maru.

A guide to sharing open healthcare data under the General Data Protection Regulation This paper, published in Nature, explores four successful open ICU healthcare databases to determine how open healthcare data can be shared appropriately in the EU. Based on the approaches of the databases, expert opinion, and literature research, four distinct approaches are outlined to openly sharing healthcare data, each with varying implications regarding data security, ease of use, sustainability, and implementability. The four approaches also have different implications for the different stakeholders: the patient, the user and the data owner. Recommendations for sharing open healthcare data are: 1. Multidisciplinary team - Legal, ethical, clinical, technical, and economical experts are necessary 2. External parties - To properly assess privacy concerns, external parties should be involved 3. De-identifcation strategy - Recommend to de-identify patient data with K-anonymisation 4. Legal basis - Consent is not required. When pseudonymising use another legal basis 5. Transparency - Be transparent about what, how, and why, data will be shared 6. Commitment of hosting institution - The hosting institution should be able and willing to aid in resolving unforeseen obstacles 7. Sharing portal - Data should be in the cloud and not downloadable. Otherwise, strict governance is required ----------------------------------------- de Kok, J.W.T.M., de la Hoz, M.Á.A., de Jong, Y. et al. A guide to sharing open healthcare data under the General Data Protection Regulation. Sci Data10, 404 (2023). https://lnkd.in/ehhyRVDT Supplementary information - https://lnkd.in/eg9VXRjn #digitalhealth #healthdata #GDPR #pseudonymisation #anonymisation #ICU #intensivecare #cloud #datagovernance

As the #healthcare industry continues to explore the transformative potential of large language models (#LLMs), one area that remains critical yet underleveraged is the role of #standardized #ontologies such as SNOMED International, LOINC, and #RxNorm. While #LLM excel at parsing unstructured clinical narratives, they often generate outputs with high variability—making #interoperability and reproducibility a challenge. That’s where standardized medical ontologies come in. By applying these coding systems as a normalization layer on top of LLM-generated outputs, we can enhance semantic consistency, data reliability, and #EHR integration. These ontologies can help bridge the gap between free text and structured data—unlocking the full potential of LLMs in clinical decision support, population health, and quality reporting. Of course, these ontologies are not without limitations—but their foundational role in standardizing terminology and reducing downstream ambiguity cannot be overstated. SNOMED CT and other standards offer a roadmap toward safer, more interoperable #AI in healthcare. #Healthinformatics #ClinicalInformatics #dataanalytics #Data