Alex Fairweather writes about how to facilitate interoperability and improve patient outcomes through the strategic use of AI in healthcare.
The ability to connect systems and share data across the healthcare ecosystem isn’t just a technical requirement, it’s the foundation for delivering truly patient-centred care. This insight captures why interoperability has become the cornerstone of modern healthcare transformation yet remains one of its greatest challenges. As healthcare rapidly digitises, strategic implementation of artificial intelligence offers unprecedented opportunities to overcome these barriers, revolutionising how we deliver care.
The interoperability challenge in healthcare
Interoperability is the ability of different systems, applications, and devices to access, exchange, and collaboratively utilise information. The lack of standardisation across healthcare systems creates significant barriers, with disparate platforms often using varying formats and protocols that complicate seamless information exchange. This fragmentation results in data silos where valuable patient information remains trapped within isolated systems.
Beyond technical challenges, interoperability faces additional obstacles. Data security concerns naturally arise when sharing sensitive patient information, regulatory compliance adds complexity, and perhaps most significantly, cultural resistance emerges from practitioners accustomed to traditional workflows. As Paul Kovalenko, chief technical officer of software development company Langate, notes: “This lack of interoperability creates significant challenges for healthcare providers, patients, and the industry at large. The inability to effectively share and exchange data can lead to delayed care, redundant testing and even medical errors.”
Yet the promise of true interoperability is compelling: improved patient care through complete information access, reduced medical errors, greater operational efficiency, and enhanced regulatory compliance. The question becomes not whether to pursue interoperability, but how to overcome these entrenched challenges.
AI as an enabler of healthcare interoperability
Artificial intelligence offers powerful capabilities to bridge interoperability gaps that traditional approaches cannot. AI can normalise inconsistent data formats, extract meaning from unstructured information, recognise patterns from large data pools, and create connections between previously siloed systems.
Real-world implementations demonstrate AI’s transformative potential. University of Michigan Health-West pioneered AI-powered documentation using Nuance Dragon Ambient eXperience (DAX), an example of ambient intelligence and scribe technology, which automatically captures and contextualises patient encounters. This approach removes the computer as a barrier between providers and patients, enabling better engagement and relieving administrative burden. Such systems have been shown to reduce documentation.
Similarly, the Mayo Clinic implemented an in-basket augmented reply technology where generative AI drafts responses to patient portal messages. In initial studies, nursing users spent approximately 30 seconds less per message when using AI-generated drafts. Importantly, these messages were rated as more empathetic and effective at addressing patient needs, showing how AI can enhance – not replace – human capabilities.
This reflects a crucial principle in healthcare AI adoption: technology should augment human expertise. As Ken Odak Odumbe, founder and director of SPID Consulting, a firm that specialises in management consulting for development and humanitarian organisations in Africa, Europe and Latin America, writes, AI should “complement, not replace, human expertise”.
The most successful implementations view AI as an intelligent assistant that amplifies clinical judgment and streamlines workflows, allowing healthcare professionals to focus on patient relationships. Ultimately clinicians, and all humans for that matter, possess an innate ability for creative and intuitive application of information in individual contexts, the way in which AI works cannot fully replicate this, meaning the role of AI to support and make clinicians more effective is crucial.
Strategic implementation: creating a path forward
Successfully integrating AI to enhance interoperability requires a thoughtful, strategic approach. Organisations should begin with small-scale pilot projects that integrate AI into existing frameworks, allowing teams to identify challenges and refine approaches before broader implementation.
Leadership plays a critical role in creating a supportive environment for innovation. Research identifies several leadership theories that facilitate innovation in healthcare, including creating a psychological climate for innovation, supporting team reflectivity, and fostering an organisational culture that embraces change. Leaders must walk the walk, encouraging experimentation while ensuring proper governance.
Stakeholder engagement from the beginning is essential. This includes involving both clinical staff and patients in the planning process. The generational divide between tech-savvy junior staff and experienced clinicians can become an asset rather than a barrier when leaders facilitate collaborative dialogue.
Additionally, strategic partnerships with technology providers can accelerate innovation. Collaboration with specialists in AI solutions provides access to cutting-edge applications and expertise that would be challenging to develop internally.
Looking ahead
The journey toward healthcare interoperability enhanced by AI isn’t without challenges, but the potential rewards – improved patient outcomes, enhanced efficiency, and more personalised care – make it essential. As healthcare evolves, those who strategically implement AI to address interoperability will not just adapt to the future; they’ll help create it.
Ask yourself: how could your organisation leverage AI to break down data silos and create more seamless patient journeys? The technologies exist today to transform this vision into reality, enabling us to enhance both the experience and outcomes for our patients.
