The healthcare sector stands on the brink of a significant transformation brought about by advancements in artificial intelligence (AI). While the pace of progress has been impressive, many stakeholders argue that the anticipated returns on investment remain elusive. Although healthcare organizations are increasingly investing in AI to enhance care delivery efficiency, only a handful have reported the desired financial or operational improvements. The disconnect between investment and actual impact is not rooted in technology itself; rather, it stems from the foundational architecture of healthcare organizations. How these entities navigate this challenge will determine whether AI alleviates the burdens faced by clinicians or simply adds another layer of expense and complexity to already strained systems.
AI has proven effective in specific areas such as documentation, coding support, and streamlining administrative tasks. However, isolated enhancements often fail to drive comprehensive transformation across the enterprise when they are merely added onto outdated and inefficient workflows without a fundamental redesign of care delivery practices.
The challenge of layering additional tools
The instinct to build upon existing frameworks in healthcare is understandable. Decision-makers often enhance Electronic Health Record (EHR) workflows with new tools. Unfortunately, many contemporary digital solutions complicate legacy processes. This layering strategy is akin to attempting to fit advanced software into a system designed decades ago, resulting in increased burdens rather than easing them. When AI-powered software is introduced atop antiquated workflows, it merely shifts challenges onto the end-users, typically clinicians. Additional administrative duties detract from the time clinicians can dedicate to patients, and merely automating ineffective workflows results in a faster, pricier version of the same issues.
For real change to occur, healthcare leaders must shift away from merely adding AI tools on top of outdated systems and instead rethink their workflows entirely.
Consequences of maintaining the status quo
There’s no specific tipping point that signals the over-layering of legacy systems; rather, the costs accumulate gradually. This manifests in more platforms and point solutions, each aiming to automate healthcare in fragmented ways. Beyond escalating expenses, several indicators signal a deteriorating system:
1. Clinical attrition: New AI tools are supposed to ease the workload for clinicians, yet one study indicated that fewer than half of these tools have improved their productivity in the last two years. Tools that create more challenges than solutions can lead to clinician frustration and even attrition from the profession.
2. Decline in capacity amid increasing demand: As the population ages, the need for care continues to climb. If AI tools complicate existing processes, clinicians will find even less time for patient interactions.
3. Patient dissatisfaction: At the heart of healthcare is the patient experience. Overworked clinicians can compromise patient care quality, leading to a decline in patient satisfaction.
Rethinking the role of AI in healthcare
In medical practice, addressing superficial symptoms without tackling root causes is deemed ineffective. Despite this, healthcare organizations often resort to quick software fixes to mask workflow issues. Leaders in healthcare must accurately diagnose the underlying problems and reassess which IT metrics truly convey success. Simply measuring adoption rates may obscure the reality; the real value will emerge from reducing friction in core workflows. Metrics that matter most include the elimination of redundant tasks, reclaimed clinical capacity, patient engagement, and clinician satisfaction, which should guide the evaluation of AI implementations.
Healthcare finds itself at a pivotal moment. The future will belong not to those who amass the most technology, but to those who harness it to reduce workloads. The current advancements in AI present a formidable opportunity for the sector to innovate, reconfigure, and renew itself.
Organizations that undertake the comprehensive redesign of care delivery workflows—rather than merely stacking on new technology—will unlock the true potential of AI. The time to rebuild infrastructures for improved care is now, rather than simply bolting on solutions.
Richard Atkin, CEO of Greenway Health, has a wealth of experience spanning 25 years in software leadership. He is dedicated to ensuring that the organizations he guides prioritize customer-centric strategies. Atkin’s background includes roles in the defense sector and healthcare technology companies, reflecting his commitment to enhancing customer outcomes and achieving excellence in product delivery. He holds a Bachelor of Science with Honors in Physics and Engineering from Bangor University and an MBA from Imperial College, London.
