The Data-Ready Foundation for an AI-Powered Healthcare Future

The case for AI in healthcare has moved beyond theoretical potential to a necessity. Clinicians spend significant time on documentation and administration during every care encounter. Medical knowledge doubles every 73 days, creating an almost impossible cognitive burden to stay up to date. Meanwhile, healthcare costs continue to spiral upward, demanding solutions that improve quality while containing expenditure.

Yet the question confronting healthcare leaders today is not whether to adopt AI, but how to implement it to deliver sustainable value rather than creating new complexities.

Growing evidence of AI’s impact on healthcare makes it critical to any future healthcare strategy. Recent studies show that AI-assisted clinical documentation can save clinicians time while improving productivity and patient focus. Predictive analytics used for population health programs can identify high-risk patients. Diagnostic AI can detect diseases earlier and with greater accuracy. Assisted coding can reduce billing errors and accelerate revenue cycles.

The Data-Ready Imperative

There is a catch, however. Artificial intelligence is only as powerful as the data infrastructure that supports it. An AI algorithm trained on fragmented, inconsistent data will produce fragmented, inconsistent results. And multiple AI tools bolted onto legacy systems may create new complexities and negative user impacts.

A data-ready approach centres on ensuring data completeness and continuity across the care continuum. This means creating unified data architectures where patient information flows seamlessly across systems and departments, while maintaining the flexibility to integrate with external health information exchanges as they evolve.

Equally critical is establishing robust data quality and governance frameworks that ensure information accuracy, security and compliance. These foundational capabilities create the integration points necessary for both current operations and future AI systems to access the comprehensive, longitudinal patient context they require to deliver meaningful insights.

Organisations with mature data foundations (e.g., governance and data platforms) achieve measurably better AI outcomes. In BCG’s 2024 DAICAMA survey, leading organisations scaled four times more use cases and realised five times greater average financial impact than laggards. Conversely, Gartner predicts that through 2026, organisations will abandon 60 per cent of AI projects that are not supported by AI‑ready data. Mature data foundations support not just today’s AI applications, but tomorrow’s innovations that we cannot yet imagine.

Inbuilt AI: The Strategic Difference

Traditional approaches to healthcare AI follow a bolt-on model – purchasing point solutions from multiple vendors, each addressing a specific use case and requiring separate integration, governance and management. This approach may appear expedient at first, but it creates long-term challenges that ultimately limit AI’s transformative potential.

The alternative – a solution with inbuilt AI – embeds intelligence directly within the electronic health record platform. This approach delivers several strategic advantages. Unified solutions eliminate the workflow friction that drives clinician resistance. Unified governance simplifies oversight and ensures consistent policy application. Consolidated vendor management reduces complexity and often the total cost of ownership. Most importantly, AI algorithms gain direct access to comprehensive patient data, enabling more accurate insights and recommendations.

InterSystems has supported healthcare transformation globally for over four decades. Our TrakCare® platform is deployed in more than 600 hospitals worldwide in 29 countries, and our healthcare solutions help manage over one billion health records across the world. With InterSystems IntelliCare™, we have created an AI-at-centre architecture that eliminates integration complexity while delivering the intelligent automation that clinicians need: ambient clinical documentation, assisted coding and clinical workflow support.

A Call to Strategic Action

Healthcare leaders face a choice that will define the next decade for their organisations. The path of fragmented AI adoption, multiple vendors, complex integrations and disparate governance may satisfy short-term pressures but creates long-term technical debt that becomes increasingly difficult to manage.

The alternative path requires discipline and strategic thinking. It means investing in data readiness before rushing to deploy AI applications. It means selecting architecture that unifies rather than fragments. It means partnering with vendors who understand the unique requirements of healthcare and who commit to long-term relationships, not transactional sales.

This is precisely the moment when strategic choices matter most. The organisations that get this foundation right will achieve improved quality, enhanced efficiency, innovation leadership and, ultimately, better health outcomes for the populations we serve.

The question is not whether AI will transform healthcare. The question is which organisations will lead that transformation, and which will struggle to keep pace because they built on the wrong foundation.

About the Author

Darren Jones is Country Manager, Australia and New Zealand for InterSystems, a creative data technology provider powering more than one billion healthcare records globally. Based in Melbourne, Jones leads the company’s ANZ operations and supports the success of InterSystems customers.

^{Disclaimer: Any AI tool or AI functionality provided by InterSystems is subject to regulatory and clinical safety requirements and is not made fully available to all global markets. Please consult the AI Ethics webpage for more information on InterSystems’ approach to Responsible AI and your InterSystems representative for any specific details on jurisdictional availability.}

Top image credit: iStock.com/sturti