Digital transformation in healthcare has led to the rapid expansion of patient data across multiple platforms and systems. While this data holds great potential for improving clinical decisions and patient outcomes, it is often fragmented, inconsistent and difficult to interpret. Different formats, varying levels of structure and isolated storage systems create barriers to achieving a unified view of the patient. Interoperability standards, combined with artificial intelligence, are beginning to address these challenges by enabling streamlined, consistent and clinically useful data integration across the healthcare ecosystem.
Unifying a Fragmented Data Landscape
Modern healthcare generates data from numerous modalities, including diagnostics, lab results, biomarker tests and clinical histories. Each data point is vital for clinicians to assess patient needs accurately, yet the sheer complexity and volume can be overwhelming. The rate at which healthcare data is growing—estimated at 47% annually—adds further urgency to the need for better data harmonisation. Data often resides in both structured and unstructured formats, scattered across siloed systems that lack integration. This fragmentation risks missing critical insights during treatment planning.
Interoperability standards serve to bridge this gap by offering consistent methods and protocols for data collection, storage, exchange and retrieval. These standards make it possible to integrate information across systems, enabling clinicians to access relevant data at the point of care. Standards such as Fast Healthcare Interoperability Resources (FHIR), developed by Health Level Seven International (HL7®), play a pivotal role in enabling data consistency across global healthcare environments. Other important frameworks include DICOM, IHE and ISO, each contributing to the collective effort to represent data in a unified, accessible format.
Challenges in Standardising Healthcare Data
Implementing interoperability standards is not without obstacles. The first challenge is data access, as information is often stored in multiple silos, impeding clinicians’ ability to gather a full patient picture during decision-making. Data quality presents another issue; gaps or inaccuracies in records, such as missing radiology reports, can reduce the reliability of assessments. Variations in data collection practices between hospitals, regions or countries further complicate efforts to create a harmonised system.
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Volume and high dimensionality are also significant concerns. Healthcare datasets can be vast and complex, with individual patient data representing only a fraction of the total. This makes it difficult to extract statistically meaningful insights without sophisticated tools. In addition, ensuring alignment with current research and community standards is critical for keeping AI applications relevant and useful. Researchers must also be incentivised to participate in the development of shared resources and frameworks to promote broad adoption.
Despite these challenges, interoperability remains essential for creating conceptual relationships across incomplete or varied datasets. Standard terminologies, such as International Classification of Diseases codes, enable data to be interpreted correctly by clinicians regardless of source or format. The goal is a seamless continuum of care where patient information flows effortlessly across systems.
Addressing the Burden of Unstructured Data
A significant portion of healthcare data—estimated at 80%—is unstructured, comprising free-text notes, radiology reports and other non-standardised documents. Analysing this information is resource-intensive and often requires manual intervention, which delays clinical workflows. Addressing unstructured data is therefore a priority for healthcare IT teams working to improve efficiency and quality of care.
AI, particularly generative AI and natural language processing (NLP), has emerged as a powerful tool to automate the processing of unstructured data. One practical application is the automation of case summaries for multidisciplinary tumour board meetings, where complex cancer cases are discussed. Gathering the necessary information from fragmented sources takes significant time and effort. AI can now extract relevant data and generate tailored summaries, customised according to clinician preferences, thereby reducing administrative burden and supporting informed clinical decisions.
Crucially, while technology can automate data processing, human oversight remains essential. From initial design through to deployment and monitoring, each phase of AI integration must involve clinical experts. This ensures that outputs are relevant, accurate and contextually appropriate. The synergy between human expertise and advanced AI capabilities supports safe and effective implementation across diverse healthcare environments.
The fusion of interoperability standards and AI is transforming healthcare data management. By overcoming challenges such as fragmented systems, data heterogeneity and unstructured formats, these tools are helping clinicians access more complete and accurate patient information. International standards like FHIR, combined with generative AI, offer practical pathways to improve data integration and streamline clinical workflows. Maintaining human oversight will be key to building AI solutions that truly enhance clinical care and operational outcomes.
Source: Healthcare Transformers
Image Credit: iStock
