Chaos has always been part of healthcare. In a system where every decision can carry life-altering consequences, it’s in fact a feature more than a bug — unpredictable circumstances demand quick thinking and agility.
In recent years, however, that turbulence has taken on a new shape: persistent workforce shortages, evolving reimbursement models, the rise of AI, and a relentless stream of clinical, operational, and political uncertainties.
These aren’t episodic disruptions. This is the new baseline.
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Healthcare leaders are used to managing through crises. But the new challenge comes through building a structure that can withstand whatever comes next. It’s not enough to be reactive or even agile. The organizations that will thrive in this environment are those that invest in resilience — the capacity to adapt, absorb strain, and maintain performance in the face of volatility.
That requires moving beyond quick fixes and embracing the principles of high-reliability systems: organizations designed to anticipate failure, respond quickly to breakdowns, and learn in real time.
Complexity isn’t the problem, unmanaged complexity is
Modern healthcare is a web of interconnected systems, decisions, and stakeholders. Radiology alone — often thought of as a supporting function — has become a case study in complexity. A single scan might generate multiple findings, some of which require immediate intervention, others that carry long-term implications. Some of these findings are expected. Others are incidental.
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Managing that follow-up is anything but straightforward. Reports get missed. Communication pathways break down. Patients fall through the cracks. And because in most organizations no one “owns” the follow-up process end-to-end, accountability becomes diffuse. As a result, incidental findings—especially those with actionable risk — often never translate into action. What begins as a clinical insight becomes an unclosed loop.
These aren’t edge cases. In many health systems, the volume of incidental findings is increasing dramatically due to better imaging technologies and the ability of AI-driven diagnostics to deliver more diagnoses at a faster clip. Without systems designed to reliably close those loops, every new scan has the potential to increase liability and erode trust.
The solution isn’t to avoid complexity. It’s to build the infrastructure to handle it—reliably, repeatedly, and with transparency.
Leadership in the age of uncertainty
High-reliability healthcare systems don’t aim for perfection. They aim for resilience. That means building workflows that anticipate failure and recover stronger from it — not ones that assume the best-case scenario every time. In this model, leaders shift from trying to control every outcome to creating the conditions for consistency and adaptation.
One useful mindset shift is the “eyes on, hands off” approach — borrowed from high-reliability organizations in industries like aviation and nuclear power. Leadership still sets priorities and standards, but it resists the temptation to micromanage. Instead, frontline teams are empowered with clarity, data, and autonomy to solve problems as they emerge.
This approach is especially critical in areas where complexity scales rapidly — like radiology. As the number of actionable follow-up recommendations grows, trying to manage each one manually becomes unsustainable. It’s not just about efficiency; it’s about risk. Inconsistent or delayed follow-up on radiology findings represent a growing patient safety concern. A high-reliability system recognizes this as a systemic challenge, not an individual one. It doesn’t rely on memory, heroism, or extra effort. It designs for consistency.
A framework for resilient operations
So, what does it look like to put resilience into practice? There’s no one-size-fits-all answer, but a few principles can guide the way.
- Prioritize what matters: Focus limited resources on areas where variability creates the highest risk. In many systems, radiology follow-up is exactly that kind of pressure point.
- Build for repeatability: Create AI-enabled workflows that are designed to work the same way—regardless of who’s involved. The more critical the process, the more important it is to be boringly consistent.
- Surface data transparently: Reliability depends on visibility. Make it easy for teams to see what’s working, where follow-ups are being missed, and how performance is trending over time.
- Design to learn: A reliable system isn’t a static one. It should adapt when failure points appear, and iterate in response to frontline feedback.
Chaos isn’t going away — but it can be managed
As leaders, we can’t avoid complexity — but we can build systems that can absorb it. Resilience isn’t the absence of disruption; it’s the ability to operate through it. And in healthcare, that resilience must be designed, not hoped for.
Because when patients fall through the cracks, it’s not just a systems failure — it’s a human one. And that’s a kind of chaos none of us can afford.
Photo: Nuthawut Somsuk, Getty Images
Angela Adams, RN, has been advancing the industry by applying AI to improve healthcare outcomes for over a decade. Angela started her career as a critical care medicine nurse at Duke University Medical Center. During her time in the hospital setting, Angela became increasingly frustrated with the inefficiencies in patient care. Driven to make a broader impact, Angela looked to the emerging healthcare AI segment for solutions that would allow her to help patients as well as assist clinicians to become more effective and efficient in solving complex medical issues. She helped advance AI adoption and overcome skepticism at companies like Jvion (acquired by Lightbeam Health Solutions), where she applied deep machine learning to lower nosocomial event rates and prevent patient deterioration. She went on to create her most recent solution at Inflo Health, where she focuses on missed follow-up radiology appointments.
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