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Healthcare data technology market: Challenges in privacy and compliance. Healthcare organizations are sitting on more data than ever before - electronic health records, connected devices, claims, imaging, and real-world evidence - and the race is on to turn that raw information into something usable. That race is fueling rapid growth in the healthcare data technology market, a space built around integrating, standardizing, securing, and monetizing health data at scale. Here's a closer look at where the market stands today, what's driving it forward, and the roadblocks still standing in the way. Key Market Facts The global healthcare data technology market was valued at USD 3.1 billion in 2024 and is projected to grow from USD 4.0 billion in 2026 to USD 9.5 billion by 2033, expanding at a compound annual growth rate of 13.4% between 2025 and 2033. A few other numbers worth noting: * North America led the market in 2024, capturing a 46.5% revenue share, while Asia Pacific is on track to be the fastest-growing region going forward. * By technology, the health data integration and monetization segment dominated in 2024, holding more than 66.55% of total revenue, as providers work to unify EHRs, clinical trial data, and real-world evidence into single, usable pipelines. * The data harmonization and structuring segment, though smaller today, is expected to post the fastest growth rate of any segment over the forecast period, largely thanks to AI and NLP tools that can standardize messy, unstructured clinical data. * EHR adoption remains the backbone of this demand: as of December 2024, roughly 88% of U.S. office-based physicians were using an EMR/EHR system, with nearly 78% on a certified platform - a base that keeps generating more data to integrate and analyze. * The competitive landscape is notably fragmented, with dozens of established and emerging vendors - including IQVIA, Veeva Systems, Oracle (Cerner), Optum, Datavant, TriNetX, and Flatiron Health - all competing for share, which keeps innovation and partnership activity high. Major Industry Trends AI and advanced analytics are moving from pilot to production. Vendors are embedding generative AI and machine learning directly into data platforms rather than treating them as add-ons. Oracle's newly launched AI Data Platform, for example, is designed to help enterprises build AI-ready data lakes and deploy AI-driven applications directly on top of unified data. Global harmonization initiatives are reshaping the data landscape. Governments and health systems are investing heavily in standardizing and connecting fragmented health records. Europe's Health Data Space regulation, formally adopted in 2025, sets a legally binding framework for cross-border health data access across the EU, covering both direct patient care and secondary research use. The UK has committed roughly £600 million toward a national Health Data Research Service, while Germany has launched a similarly ambitious medical informatics funding program - all aimed at making health data more accessible for research and care. Discover more Internet & Telecom Commercial Distribution Public Relations Interoperability is becoming a regulatory mandate, not just a best practice. In the U.S., federal regulators have intensified enforcement of information-blocking rules under the 21st Century Cures Act, pushing providers and vendors to make electronic health information more freely accessible and exchangeable. Strategic partnerships and M&A are accelerating capability-building. Rather than building every capability in-house, companies are teaming up or acquiring their way into new strengths - from IQVIA and Veeva's long-term global partnership to streamline clinical trials, to Samsung's acquisition of digital health platform Xealth, to Commvault's move to acquire data security firm Satori Cyber to strengthen protections around AI training data. Social determinants of health (SDOH) and real-world data are being folded into core platforms. Partnerships like HealthEdge and Unite Us embedding SDOH data into care management platforms reflect a broader shift toward more holistic, value-based data models that go beyond clinical records alone. Emerging technologies are tackling harmonization at scale. Graph databases, federated learning frameworks, and AI-driven semantic mapping are increasingly used to link genomic, clinical, and multi-institutional datasets - enabling collaborative research without centralizing sensitive patient data. Need data specific to your market or segment? This report can be tailored with additional countries, segments, or data points to match your exact research needs - with free customization available on purchase. Request Report Customization Primary System Challenges Data fragmentation across legacy systems. Many healthcare organizations still operate with claims, patient records, and lab results scattered across disconnected legacy systems, creating operational bottlenecks and slowing down everything from claims processing to clinical research. Regulatory complexity and compliance costs. Stringent privacy and security frameworks - HIPAA, GDPR, and a growing patchwork of regional health data governance rules - demand robust compliance and audit capabilities. While these regulations push innovation in secure data management, they also add real implementation complexity and cost for both providers and vendors. Discover more Communications & Media Studies communication Interoperability gaps between platforms. Despite widespread EHR adoption, getting different systems to talk to each other in a standardized way (HL7/FHIR and beyond) remains a persistent hurdle, particularly for organizations trying to merge imaging, genomic, and unstructured clinical data. Balancing data monetization with privacy. As organizations look to unlock value from health data - through analytics, research partnerships, or licensing - they must simultaneously protect patient privacy, driving demand for de-identification, pseudonymization, and governance tools that can slow down time-to-insight. Scalability under growing data volumes. As data volume and variety expand (from wearables, genomics, and real-world evidence), organizations need cloud-native, auto-scaling infrastructure - a shift that isn't trivial for institutions still running on manual or semi-automated processes. Regional disparities in infrastructure and investment. While North America and Europe are pushing ahead with major national data initiatives, regions like Latin America and parts of MEA are still working to modernize foundational health data infrastructure, creating uneven adoption rates globally. The bigger picture: healthcare data technology is no longer a back-office IT concern - it's becoming central to how care gets delivered, how research gets funded, and how health systems compete. Organizations that solve for interoperability, compliance, and scalability today will be the ones best positioned to capture value as the market roughly triples in size by 2033. Discover more communications Business & Industrial Want the Full Picture? See who's leading the market. Get access to detailed profiles of the key players shaping the healthcare data technology space - including IQVIA, Veeva Systems, Oracle, Optum, Datavant, and more - along with their strategies, partnerships, and market positioning. 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From AI hype to AI maturity: why healthcare needs A resilient trust infrastructure. Publish Date July 23, 2026 Read Time 6 min Healthcare organizations are operating under increasing structural pressure. Administrative complexity continues to grow. Compliance and governance expectations are expanding. Labor constraints persist. And leaders are being asked to do more with fewer resources. At the same time, the conversation around AI in healthcare has shifted dramatically. Not long ago, discussions centered on risk - bias, hallucinations, privacy concerns, and unintended consequences. Today, the focus has shifted toward acceleration. Every healthcare organization is evaluating how intelligent systems can improve consistency, strengthen operational resilience, reduce administrative burden, and support more scalable operations. That shift is natural. Every transformative technology follows a similar path. What begins with skepticism often gives way to excitement, and excitement eventually becomes adoption. But while the conversation has evolved rapidly, operational maturity has not. The real divide in healthcare today is not AI versus no AI. It is resilient systems versus fragile systems. And as healthcare enters the era of agentic AI, that distinction matters more than ever. Moving beyond hype starts with precision. When someone says "AI," they're often referring to fundamentally different technologies with very different capabilities, risks, and governance requirements. Are Datavant talking about: * An LLM summarizing emails or drafting documentation? * An autonomous agent taking actions across enterprise systems? * A clinical model supporting diagnosis or treatment decisions? * OCR extracting data from scanned documents and PDFs? Lumping these together under a single "AI" label can obscure important distinctions. Effective governance begins with understanding exactly which technology is being deployed, what decisions it influences, and the risks it introduces. Why more autonomous systems require a stronger trust infrastructure. Traditional software systems generally wait for instructions. As autonomy expands, the conversation can no longer focus solely on capability. It must focus equally on measurement, accountability, and governance. Organizations need confidence that intelligent systems are operating safely, securely, and reliably. They need visibility into how decisions are made, how performance is measured, and how accountability is maintained as automation expands. Consider an AI agent supporting patient record retrieval and release of information workflows. The agent receives requests, identifies the appropriate data sources, gathers required records, validates authorization requirements, and coordinates information exchange across multiple organizations. The challenge is not simply whether the agent can complete the task. Organizations need confidence that access permissions were enforced correctly, sensitive information was handled appropriately, every action was auditable, and the agent operated within clearly defined governance boundaries. As these systems become more autonomous, trust depends on the controls surrounding the agent as much as the intelligence within it. For decades, cybersecurity matured through shared frameworks, controls, testing methodologies, and governance practices. Organizations gained confidence because they had mechanisms to evaluate risk, measure performance, and establish accountability. The governance of intelligent systems will require a similar evolution. That is why Datavant recently joined the AIUC-1 Consortium, a coalition of more than 200 security leaders, researchers, and organizations working to develop standards for agentic AI safety, security, and reliability. AIUC-1 is focused specifically on creating a trust framework for AI agents - providing organizations with a common approach to evaluating how intelligent systems operate, make decisions, and interact with critical workflows. The emergence of AIUC-1 signals something larger than the creation of another industry framework. It reflects a broader recognition that the deployment of intelligent systems at scale requires a trust infrastructure. Trust infrastructure consists of the standards, controls, validation methodologies, governance frameworks, and accountability mechanisms that allow organizations to operationalize intelligent systems confidently. Just as cybersecurity matured through shared frameworks and measurable controls, AI will require similar mechanisms to scale responsibly. For healthcare, that need is particularly acute. Healthcare organizations do not simply need faster systems. They need systems that are measurable, auditable, defensible, and aligned with existing governance structures. Trust is not a feature layered onto intelligent systems after deployment. It must be embedded in how those systems are designed, validated, monitored, and governed. When expansion velocity outpaces risk velocity. One way to think about this challenge is through the lens of expansion velocity and risk velocity. As intelligent systems become more capable, organizations gain the ability to automate more workflows, connect more systems, and act on more data (expansion velocity). The question is whether governance, accountability, and oversight are scaling at the same rate. That's risk velocity. When expansion velocity outpaces risk velocity, organizations introduce fragility. When they scale together, organizations build resilience. As the speed of AI expansion increases, healthcare-specific risk controls must scale at the same rate. That principle sits at the heart of AI maturity. Healthcare leaders do not need to slow AI adoption. But they do need to ensure that controls scale alongside capability. As intelligent systems gain access to more data and more workflows, governance can no longer be an afterthought. Why healthcare must help shape the standards. Healthcare presents unique challenges that cannot simply be inherited from other industries. Patient trust, data sensitivity, regulatory accountability, and operational resilience are foundational requirements, not secondary considerations. As organizations deploy increasingly intelligent systems across clinical, administrative, and operational environments, they need standards that reflect those realities. They need confidence that intelligent systems are operating within clearly defined boundaries and they need transparency around data use, accountability for decisions, and governance mechanisms that can scale alongside increasing levels of automation. That is why Datavant joined AIUC-1 not simply as a participant, but as a contributor. Datavant believe healthcare's requirements around privacy, operational resilience, and accountability should help shape the standards that govern the next generation of intelligent systems. Datavant has spent years helping healthcare organizations securely exchange and use data at scale. As AIUC-1 continues to evolve, Datavant has an opportunity to help ensure that emerging standards are practical, actionable, and aligned with the realities of healthcare operations. The goal is not to slow innovation. The goal is to ensure innovation remains trustworthy as it scales. The future will belong to resilient AI. AI, as with every major technological shift, will become part of healthcare. The opportunity - and responsibility - is to ensure the systems Datavant build are not just intelligent, but governed, secure, and resilient. Moving beyond hype requires discipline. It requires governance that scales alongside capability. Measurement that scales alongside automation. And accountability that scales alongside complexity. The emergence of AIUC-1 reflects an industry beginning to grapple with that challenge. It signals a shift away from experimentation for its own sake and toward something more durable: the standards, governance, and trust infrastructure necessary to operationalize AI responsibly at scale. The future of healthcare will not be defined by how aggressively organizations adopt intelligent systems. It will be defined by how intentionally they govern, validate, and mature those systems over time. The organizations that succeed will be the ones that scale governance alongside capability, measurement alongside automation, and accountability alongside innovation. Because in healthcare, trust is not a feature, it is the foundation.