Introduction
Healthcare systems worldwide face growing pressure from workforce shortages and aging populations. These challenges demand new approaches to care delivery. Increasingly, healthcare leaders view artificial intelligence (AI) as a solution. However, AI cannot scale effectively without reliable and standardized data.
During the HIMSS Global Health Conference & Exposition 2026, leaders emphasized that standardized data is essential for unlocking the full potential of AI in healthcare. According to Dan Liljenquist, organizations must rethink how they manage and structure healthcare data. Without consistent data standards, AI systems struggle to deliver reliable insights and meaningful clinical outcomes.
Why Healthcare Needs Standardized Data
Healthcare data often exists in fragmented systems. Hospitals use different electronic health record platforms, data formats, and documentation methods. As a result, information becomes difficult to analyze across organizations.
Standardized data solves this problem by ensuring consistency. When healthcare systems adopt common data formats and structures, AI tools can process information quickly and accurately. Moreover, standardized data reduces manual data cleaning, which consumes valuable time and resources.
Additionally, structured data improves collaboration across healthcare organizations. Hospitals can exchange patient information efficiently while maintaining accuracy and interoperability. As a result, clinicians gain better insights into patient health trends and treatment outcomes.
Interoperability as a Strategic Priority
For many healthcare organizations, interoperability has traditionally been viewed as a compliance requirement. However, experts now argue that interoperability should become a strategic priority.
Healthcare systems that treat interoperability strategically can integrate AI tools more effectively. When data flows smoothly between systems, AI models can analyze larger datasets and deliver more accurate predictions.
Regulatory momentum also supports this transformation. Agencies such as the Centers for Medicare & Medicaid Services are encouraging healthcare providers to adopt stronger data-sharing frameworks. These initiatives aim to improve data exchange and accelerate digital health innovation.
Consequently, organizations that invest in interoperable systems today will gain a significant advantage as AI adoption expands across the healthcare sector.
Building Computable Data for AI
Interoperability alone is not enough to support AI. Healthcare organizations must also focus on creating “computable data.” Computable data refers to information that is structured, standardized, and easily interpreted by machines.
Even when hospitals exchange data through standards such as Fast Healthcare Interoperability Resources, the information often requires manual cleanup before AI systems can analyze it. This extra step slows innovation and reduces efficiency.
To address this challenge, healthcare organizations are building unified data models that normalize information across systems. These models allow AI tools to interpret patient data consistently, regardless of the source system.
As a result, healthcare leaders are increasingly investing in data engineering and semantic modeling to improve data quality and AI readiness.
Cloud Data Platforms and AI Adoption
Many health systems are moving toward cloud-based data platforms to support AI initiatives. For example, Intermountain Health has implemented a cloud strategy that extracts data from electronic health records and standardizes it within a unified platform.
This approach allows organizations to process large volumes of healthcare data daily. Furthermore, cloud environments enable AI systems to analyze information at scale.
Centralized data platforms also simplify governance and security. Healthcare organizations can enforce consistent data policies while maintaining compliance with privacy regulations.
As cloud adoption continues to grow, it will likely become the backbone of large-scale healthcare AI deployments.
Reducing Administrative Costs with AI
Administrative complexity remains one of healthcare’s biggest financial challenges. Experts estimate that billions of dollars are spent annually on administrative processes caused by fragmented data systems.
AI can help reduce these costs. Automated systems can streamline workflows such as documentation, claims processing, and patient scheduling. However, these efficiencies depend on clean and standardized data.
When healthcare data becomes consistent and structured, AI tools can automate routine tasks more effectively. This improvement frees clinicians from administrative burdens and allows them to focus on patient care.
Ultimately, reducing administrative waste could unlock significant resources for improving healthcare delivery and patient outcomes.
Future Impact of AI-Driven Healthcare
Standardized data will play a central role in the future of healthcare innovation. With strong data foundations, AI can support clinical decision-making, predict disease progression, and improve population health management.
For example, AI systems could identify medication risks earlier, detect chronic disease patterns, and recommend personalized treatments. These capabilities could transform healthcare delivery and improve patient outcomes.
However, healthcare organizations must first invest in strong data infrastructure. Without standardized and computable data, AI systems cannot deliver consistent results.
Therefore, leaders increasingly recognize that data strategy—not just technology—will determine the success of AI in healthcare
Conclusion
Artificial intelligence has the potential to reshape healthcare delivery. Yet the success of AI depends on the quality and consistency of healthcare data.
Standardized data enables interoperability, reduces manual data processing, and supports scalable AI applications. Healthcare organizations that build strong data foundations today will lead the next wave of digital health innovation.
As healthcare continues to evolve, standardized and computable data will become the strategic engine powering AI-driven care.
