Healthcare has reached an inflection point with artificial intelligence. Advances have been swift. But many would argue that returns on investment are less evident. Healthcare organizations are exploring AI to improve efficiency of care delivery, yet few report seeing the financial or operational outcomes they expected. The gap between investment and impact is not a technology problem. It is an architecture problem, and how healthcare organizations respond to it will determine whether AI becomes the thing that finally relieves clinician burden, or one more layer of cost and complexity stacked on top of systems that were never built to support it.
AI has already demonstrated meaningful value in areas such as documentation, coding assistance, and administrative automation. Yet isolated improvements rarely translate into enterprise-wide transformation when they are layered onto outdated, inefficient workflows instead of being integrated into redesigned care delivery.
The layering trap
It seems logical to build on top of something that is already in place. You start with a familiar foundation and work your way up. In healthcare, decision-makers do this by adding tools onto existing EHR workflows. But many of the digital tools being layered on today are adding complexity to legacy processes. The layering approach is essentially an attempt to apply 2026 technology to a 1990s operating system. When new software, powered by artificial intelligence, is added on top of these legacy workflows, it doesn’t relieve pressure points; it shifts them towards end users, and oftentimes those are clinicians. Every additional administrative task takes time away from patient care. Automating an inefficient workflow simply creates a faster, more expensive version of the same problem.
Healthcare decision-makers need to recognize that this pattern will keep repeating itself until organizations stop adding AI on top of legacy systems, and start reimagining entire workflows.
The cost of inaction
The breaking point isn’t a single calamity. There is no specific moment that announces, “You’ve layered too much on top of a legacy system.” Instead, the cost shows up gradually, in more platforms, and more point solutions, each trying to automate healthcare piece by piece. Beyond mounting expenses, the following are signs of systemic decline:
- Clinical attrition: Does a new AI tool make life easier for clinicians? One study said less than half of new AI tools over the past two years have made providers more productive. If a tool makes work more arduous, clinicians will feel the burden. If this continues, it can ultimately lead to them leaving the profession.
- Capacity drop vs demand rise: Demand for care continues to rise as the population ages. If AI tools make a process more difficult, clinicians will have even less time to focus on patient care.
- Patient dissatisfaction: Healthcare is ultimately about patients. If clinicians administering care are overworked and overburdened, patient experience will suffer.
The healthcare AI pivot
In medicine, treating a symptom while ignoring the underlying cause is considered poor practice. Yet, healthcare organizations are increasingly applying software band-aids to cover workflow inefficiencies. Healthcare leaders need to understand how to diagnose the underlying problem. To do that, they should reconsider which healthcare IT metrics are most important. Adoption rates don’t always tell the real story. Value will be realized through reductions in friction within core workflows. Metrics like work eliminated, reclaimed clinical capacity, patient engagement, and provider satisfaction should carry the most weight in determining the benefits from AI innovation.
Healthcare is at an inflection point. Its future doesn’t belong to those with the most technology; it belongs to those who utilize technology to eliminate workload. The current moment in artificial intelligence gives healthcare one of its most significant opportunities to reimagine, re-engineer, and reinvigorate this critical sector.
Organizations that redesign care delivery workflows – not simply layer on more technology – will be the ones that realize AI’s full promise. It’s time to rebuild, not bolt on.
Photo: Irina_Strelnikova, Getty Images
With 25 years of leadership in software organizations, Richard Atkin, the CEO of Greenway Health, has remained focused on ensuring that the businesses he leads are customer-driven. He believes that a culture of clear focus and alignment with customer needs is the cornerstone of excellence in product delivery, driving improvements in customer outcomes.
Richard began his career in the defense industry before transitioning into healthcare technology, where he held roles at Datex-Ohmeda (now part of GE Healthcare), Misys Hospital Systems, Sunquest, and Greenway Health. Throughout his tenure in healthcare IT leadership, Richard has driven team growth, transformation, innovation, and a commitment to customer-focused excellence. Immediately before rejoining Greenway Health as CEO in 2025, Richard served on the Greenway Board of Directors, maintaining a strong connection to Greenway’s mission, vision, and core values. Richard earned a Bachelor of Science with Honors in Physics and Engineering from Bangor University, Wales and an MBA from Imperial College, London.
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