The mantra echoing throughout the healthcare AI discourse and vendor pitches is nearly universal: “AI must integrate into existing clinical workflows.” This well-intentioned guidance aims to minimize disruption and maximize adoption. But this approach fundamentally misunderstands what makes transformative technology actually transformative.

Let me be clear: if we truly want AI to revolutionize healthcare rather than merely digitize our dysfunction, we must embrace workflow redefinition, not just integration.

The Integration Trap

Current healthcare AI implementation largely focuses on fitting new technologies into existing processes. We add AI-powered documentation tools to help clinicians write notes faster. We implement algorithm-based triage systems within existing ED workflows. We layer predictive analytics onto existing chronic disease management programs.

This approach produces modest gains at best and, at worst, adds complexity to already burdensome workflows. The results speak for themselves: despite billions invested in healthcare AI, we haven’t seen the revolutionary improvements in outcomes, efficiency, or costs that we’d expect from truly transformative technology.

Lessons from Industries That Got It Right

Airlines: From Check-In Counters to Self-Service

In the 1990s, buying a plane ticket meant visiting a travel agent, browsing flight options in physical catalogs, and manually booking through an agent who worked with airlines over the phone. If airlines had followed healthcare’s approach, they would have digitized travel agency booking systems—but they didn’t.

Remember when air travel meant standing in long lines at check-in counters, where agents manually entered your information and printed boarding passes? Airlines didn’t simply add computers to speed up this process—they fundamentally reimagined it.

✅ Online booking platforms replaced travel agencies—now, customers could book directly. Etihad Airways is introducing AI-powered booking through chat apps, allowing customers to make reservations using natural language interactions. This shift eliminates the need for complex booking interfaces and streamlines the entire process.
✅ Dynamic pricing models replaced fixed ticket prices—AI now determines seat pricing in real-time.
✅ Self-check-in kiosks and mobile boarding passes eliminated the need for ticket counters.

If airlines had simply digitized their existing system, we’d still be calling travel agents today. Instead, they rethought the process—and travel became faster, easier, and more affordable. Delta Airlines didn’t just make their check-in agents more efficient; they transformed passengers into active participants in the process, reducing staffing needs by 40% while improving satisfaction scores.

The result wasn’t just a faster version of the old workflow—it was an entirely new model of service delivery.

Real-time Optimization: Some airlines are using AI to optimize flight routes in real-time based on weather conditions, potentially saving millions in fuel costs. This approach completely redefines how flight planning and operations are conducted.

Predictive Maintenance: AI is being used to predict when aircraft components need maintenance, shifting from scheduled to predictive maintenance models. This not only reduces downtime but also enhances safety.

With the introduction of digital systems, airlines redefined the entire passenger experience. Self-service kiosks, mobile check-in, and digital boarding passes eliminated entire workflow steps.

👉 Healthcare must do the same. Instead of using AI to simply document patient visits faster, we should ask: Do we need traditional documentation at all? Can AI create real-time clinical decision support instead?

Banking: From Tellers to Apps

Banking offers another instructive example. When ATMs first appeared, they were seen as supplements to human tellers—technology integrated into existing workflows. But forward-thinking banks recognized a bigger opportunity.

Instead of just making transactions faster, digital banking reimagined the entire customer relationship. Mobile apps don’t merely replicate teller functions; they create entirely new service models. JPMorgan Chase reduced physical branches by 20% while growing their customer base through digital transformation.

Today, most banking interactions occur without any human intervention, not because the technology made tellers faster, but because it eliminated the need for that workflow entirely.

👉 What if we applied this thinking to healthcare? Instead of just making scheduling easier with AI, why not eliminate the need for many in-person visits altogether through AI-driven remote diagnostics?

Retail: From Cashiers to Seamless Commerce

Amazon Go stores represent perhaps the ultimate workflow redefinition. Rather than improving checkout efficiency, they eliminated the checkout process altogether. Cameras and sensors track purchases, and customers simply leave with their items—no lines, no scanning, no payment process.

Traditional retailers who merely added self-checkout lanes (integrating technology into existing workflows) achieved modest efficiency gains. But Amazon’s complete workflow redefinition delivered a transformative customer experience while reducing labor costs by approximately 75% per store.

👉 In healthcare, why do we still rely on human triage nurses for every patient? What if AI could predict patient needs and preemptively arrange care instead of just assisting human triage?

Healthcare’s Missed Opportunity

Why hasn’t healthcare achieved similar transformations? We keep digitizing broken processes rather than reimagining care delivery.

Consider telehealth implementation. Most providers simply replicated in-person visits via video, maintaining the same scheduling, documentation, and follow-up workflows. The more innovative approach—asynchronous care models with AI-powered triage, automated follow-up, and continuous monitoring—remains the exception rather than the rule.

Or look at prescription management. We’ve digitized prescriptions but maintained the same basic workflow: doctor writes prescription, sends to pharmacy, patient picks up medication. Forward-thinking companies like Amazon Pharmacy and PillPack redefined this entire process with automated refills, home delivery, and medication management tools, reducing prescription abandonment rates by nearly 50%.

What True Workflow Redefinition Looks Like in Healthcare

What would transformative AI implementation look like in healthcare? Here are some examples:

1. Diagnostic Reimagination: Instead of using AI to help radiologists read images faster, redesign the entire diagnostic pathway. Automated primary reads with human oversight only for edge cases could reduce diagnostic times from days to minutes while freeing specialists for more complex work. UK’s Moorfields Eye Hospital partnered with DeepMind to implement such a system, reducing unnecessary referrals by 54%.
2. Continuous vs. Episodic Care: Rather than scheduling regular check-ups regardless of patient status, implement continuous monitoring with AI-driven alerts that trigger interventions only when needed. Kaiser Permanente’s virtual cardiac rehabilitation program redesigned traditional rehab workflows, improving completion rates from 50% to 87% while reducing costs by 63%.
3. From Reactive to Predictive: Instead of treating exacerbations, redefine chronic disease management around prediction and prevention. Partners HealthCare implemented AI-driven predictive models for heart failure, triggering interventions days before clinical deterioration, reducing admissions by 44%.

The Path Forward

For healthcare leaders ready to embrace true transformation, I offer these guiding principles:

1. Start with outcomes, not processes: Ask “what are we trying to achieve?” rather than “how can we make our current process faster?”
2. Empower new stakeholders: Just as airlines made passengers active participants in check-in, identify opportunities to shift responsibilities to patients, automated systems, or non-traditional care team members.
3. Challenge assumptions: Question whether each step in current workflows remains necessary when AI capabilities are available.
4. Pilot radical redesigns: Create safe spaces to test completely new care delivery models rather than just technology-enhanced versions of current processes.

The Path Forward: Rethink, Don’t Retrofit

The most valuable question in AI implementation isn’t “How do we integrate this into existing workflows?” but rather “Which workflows should no longer exist?”

The true promise of AI in healthcare isn’t to make doctors and nurses marginally more efficient within broken systems. It’s to fundamentally reimagine care delivery, creating models that are more accessible, efficient, and effective than anything we could achieve by simply digitizing current processes.

Industries that merely integrated technology into existing workflows achieved incremental improvements. Those that redefined their entire operational model achieved transformation.

If we want AI to truly transform healthcare rather than simply digitize our dysfunction, we must be willing to reimagine healthcare delivery from the ground up. It’s not about the workflow—it’s about having the courage to recognize when the workflow itself is the problem.