Digital health
the practical reality: “Integration is hugely important. If it’s not within the systems we’re already using, those that are keen to use it will use it because they’re excited about a new tool. But for most people, you’ve got to make it easy for them to use.” This is perhaps the most significant
technical and operational hurdle to widespread AI adoption. An AI tool, no matter how sophisticated, is of limited value if it cannot communicate with the electronic patient record, clinical decision support systems, or the various other digital platforms that underpin modern clinical workflows. The goal must be to create end-to-end AI-
powered workflows that are embedded within the existing digital ecosystem. This means moving beyond standalone AI applications and developing integrated solutions that can pull data from multiple sources, perform analysis, and then push results back into the relevant system in a structured and useful format. Achieving true integration requires more
than technical capability. It demands deep understanding of healthcare’s unique workflows, regulations, and operational realities. AI providers must possess genuine domain knowledge to build solutions that fit naturally into existing practice. This expertise is invaluable in understanding not just what data exists, but how practitioners actually work, what information they need at specific points in their workflow, and how different clinical settings vary in their requirements. System integration is not just a technical
challenge; it’s also a workflow challenge. The introduction of a new AI tool can disrupt established ways of working, and if not managed carefully, can create more problems than it solves. Essential steps include mapping existing workflows, identifying specific points where AI can add value, and redesigning processes in ways that are intuitive and efficient for end users. This requires collaborative approaches that bring together AI developers, IT teams, and frontline clinicians to co-create solutions.
Funding for the long term Less than 58% of healthcare respondents say AI adoption is a strategic priority with clear ownership at board level, highlighting a significant gap between frontline enthusiasm and organisational readiness. This disconnect is compounded by the persistent challenge of funding, identified by 44% of healthcare professionals as a primary barrier to adoption. Despite renewed commitments in the NHS 10
Year Health Plan to reserve at least 3% of annual spend for digital transformation and service innovation, local leaders frequently report lack
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of clarity about which innovation funds are available, how they are accessed, and how long they will last. AI initiatives must compete not just with other technology projects but with more immediate operational crises such as staffing gaps, elective backlogs and estates pressures, that inevitably dominate investment decisions. This reinforces the importance of robust business cases built around tangible outcomes: workforce time released, reduction in administrative burden, improved quality or safety metrics. Demonstrating these through defined use cases and their efficiency benefits is increasingly critical in securing senior-level commitment. However, the funding model itself needs
to evolve. Traditional capital expenditure approaches characterised by large, one-off investments in IT systems, are poorly suited to the dynamic, iterative nature of AI technology. An emerging alternative treats AI as part of operational expenditure, positioning it not as a static product but as part of a digital workforce. This shift from capex to opex thinking
encourages longer-term value realisation and aligns with how digital transformation is increasingly funded in other sectors. It supports flexible adoption, enabling services to trial, evaluate, expand, or discontinue use according to outcomes, without being tethered to cumbersome procurement cycles. It also lowers barriers to entry, allowing organisations to experiment with AI without huge upfront investment, while aligning the incentives of AI providers with organisational goals. The economic benefits of AI may not
materialise as direct cash-releasing savings but rather as increased capacity and improved outcomes. By automating administrative tasks, AI can free practitioners to see more patients or spend more time on complex cases, allowing organisations to do more with the same resources - a critical capability given ambitious government productivity targets.
A path forward The evidence reveals a healthcare sector moving
beyond initial hype and engaging in thoughtful exploration of AI’s genuine potential. The overwhelming consensus is that benefits are too significant to ignore, but success depends absolutely on getting the fundamentals right. Five imperatives emerge clearly: purpose- built innovation addressing real problems; rigorous ethical governance; human-first design that augments rather than replaces clinical judgement; seamless system integration; and sustainable funding models. A technically brilliant, but unethical AI, will fail. A safe, effective tool that can’t communicate with existing systems will become just another disconnected application gathering digital dust. The road ahead is undoubtedly challenging,
but by working collaboratively, remaining grounded in evidence, and keeping ethics at the core, healthcare can harness AI to build a future that is more efficient, more effective, and ultimately more human.
CSJ
Reference 1.
https://www.gov.uk/government/publications/ artificial-intelligence-sector-study-2024/ artificial-intelligence-sector-study-2024
About the author
Dr. Jon Shaw is co-founder of CareFlow Connect and System C’s director of clinical product strategy, bringing a unique combination of clinical expertise and technical innovation to healthcare technology. With degrees in both Medicine and Molecular Biology, he qualified as a surgeon and practiced for over 12 years in Surgery and Emergency Medicine within the NHS, gaining deep insight into the real-world challenges facing healthcare providers. A passionate coder since the age of 10, Dr. Shaw has dedicated his career to leveraging software solutions to connect healthcare systems and transform health and care outcomes for patients and clinicians alike, bridging the gap between clinical practice and digital innovation.
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