Category Archives: Radiology Innovation and Quality

Disruptive innovation! Six-sigma! DMAIC! Sustainable growth rate!  These are words people throw around, but it’s the idea that counts, not just the acronyms.

Navigating AI Decisions: Going Beyond “Buy vs Build”

Conceptual illustration of an AI buy-versus-build crossroads with governance and monitoring decisions

“Should we buy or build?” is usually the first question organizations ask about radiology AI. It is useful, but it is no longer the whole question.

Today, you can choose from cleared clinical algorithms, enterprise AI platforms, generative-AI tools, and internally developed workflows. The real challenge begins after a contract is signed or a prototype works. AI becomes another clinical system: one that can help, distract, confuse, or quietly change behavior.

Anyone who has lived through an enterprise IT rollout, PACS replacement, or hospital merger (let alone lead an aspect of it) knows the pattern. Installation is only one part of deployment. The hard work is making a tool fit the people, data, incentives, exceptions, and failure modes of the place where it will be used.

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AI Reimbursement Is Becoming a Workflow Problem

CMS has proposed a new way to recognize and potentially pay for certain software-based clinical services (always start with the fact sheet – far more readable than the full text). This may matter in radiology, but it does not mean every AI tool will suddenly be reimbursed by Medicare.

The proposal calls these tools Software as a Medical Service. Examples include software that extracts useful information from medical images, such as heart blood-flow analysis, fracture-risk scores, or brain MRI comparisons. CMS would give some of these services their own payment category while it learns more about how they are used.

Medicare is beginning to acknowledge that some software can provide clinical information beyond a simple workflow aid, and it augments rather than replace physician work (Radiologists have been saying this for a while, and other -ologies expected to follow as product lines expand). Still, the proposal is temporary and limited. Many AI tools would not qualify for separate payment, and payment will depend on appropriate ordering, documentation, billing, and medical necessity.

Clear language matters – CMS differentiates between assistive, augmentative, and autonomous. Software that helps a radiologist work faster (assistive) is different from software that provides new, clinically useful measurements (augmentative). Both differ from a system that makes a diagnosis without a clinician (autonomous). Administrative tools such as scheduling or drafting messages can be useful, but they are not diagnostic services.

The bigger challenge is workflow. Buying or building a tool is only the beginning. All of it involve some form of cost. Hospitals must integrate it into their systems, review privacy and security, train staff, check its performance, document its use, bill correctly, manage denials, and show that it improves care. On the flip side, a billing code alone does not guarantee revenue.

For radiology leaders, the practical next step is to take inventory: Which software tools are already in use? What do they add to patient care? Are eligible services being documented and billed correctly? What do they cost, and what clinical decisions do they improve?

CMS’s proposal is a meaningful step, but not a windfall. The opportunity is to identify the software services that provide distinct value, and build reliable workflows around them.

FDA AI Guidance and the Hard Part of Transparency

The least glamorous part of AI in radiology may turn out to be the most important: telling people what changed.

That sounds simple. It is not. A diagnostic AI tool may be trained on one dataset, validated on another, deployed inside a PACS or reporting workflow, monitored after release, and then updated when the model, threshold, input, interface, or intended environment changes. Somewhere in that chain, a radiologist is expected to decide whether to trust a box, a score, a contour, a triage flag, or a sentence.

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AI Readiness as an Educational Obligation

I wrote a guest editorial in Academic Radiology about AI readiness as a practical educational obligation, rather than an optional informatics side quest.

The piece responds to a study of medical undergraduates and radiology trainees in China, but the issue is broader: enthusiasm and exposure do not equal competence. Radiologists need enough AI literacy to recognize workflow fit, automation bias, governance gaps, and the limits of machine suggestions. Most trainees will not become model developers.

They still need to become safe, skeptical operators who can use AI with confidence and accountability in real clinical environments, worldwide, across diverse resource settings today.

What Radiology Teaches Us About AI and the Workforce

Health systems frequently ask whether artificial intelligence will ultimately reduce the need for clinicians. Radiology provides one of the clearest real-world answers to date. AI changes the mechanics of work far more than it changes the need for expertise.

I had the privilege to speak with CNN on this topic recently. The story is out now.

In imaging operations, AI is already being used to support exam prioritization, improve image reconstruction, and reduce friction in routine workflows. These contributions are meaningful, particularly in high-volume environments. But they do not displace clinical judgment, professional accountability, or responsibility for patient outcomes. In practice, the performance gains attributed to AI are inseparable from expert oversight and careful integration into clinical teams.

We use AI as a capacity and quality multiplier, not as a substitute for our training. That line is difficult to walk and gets thinner as products improve, but the thought matters. Deploying it primarily as a justification for workforce reduction or skill substitution introduces avoidable risk to patient safety and physician trust.

Success ultimately go to those investing in workflow, governance structures, and building solution that define clear roles for both humans and machines. Radiology may be an early example, but the underlying lesson extends well beyond imaging.

What’s in Store for Our VA Health System?

Source: https://www.philadelphia.va.gov

Several days ago, Rear Adm. Ronny Jackson confirmed he will drop out of the confirmation process for the Veterans Affairs secretary position after Donald Trump fired the then-Secretary David Shulkin last month.

Whoever ends up taking the lead in managing the American heroes’ healthcare bears quite a heavy burden, and careful selection, approval, and confirmation process is warranted. Continue reading

Your Origin Story for Data Science

There’s an origin story for every superhero; even those without superpowers (like Batman – that’s right) got started somewhere. What we sometimes forget is that there is also an origin story for every regular person, every profession, every hobby.

Source: Wikimedia Commons

 

If you’re a radiologist looking to learn a few things in radiology data science, a simple web search will reveal a seemingly overwhelming amount of material you might have to know.

Fortunately, only a very small subset is necessary to start being productive.  Here are a few resources I used to get started.

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The Agitator, Innovator, and Orchestrator Model

A well-written framework on Stanford Social Innovation Review describes three distinct forces of transforming a practice.

An agitator brings the grievances of specific individuals or groups to the forefront of public awareness. An innovator creates an actionable solution to address these grievances. And an orchestrator coordinates action across groups, organizations, and sectors to scale the proposed solution.

The key observation is that transformation requires all three in harmony.  In medicine, the voices of agitators frequently meet top-down repression or with the silence of the leadership. “This is just the way we’ve always done it,” they might say.

The Stanford article focuses on building a team consisting of people in all three domains in order to bring about social innovation.  In medicine, practices tend to be resistant to change partly due to the higher stakes but also due to the highly regulated climate of modern health care.  (This is not necessarily good or bad – it just is.)

Although medicine often places more weight on orchestration – coordination of interdisciplinary care to benefit patient health – it stands to reason that a healthy dose of the other two is also necessary. If you see yourself as an agitator, know that a thorough understanding of stakeholder analysis can help you better differentiate between a simple inconvenience and an opportunity to create value. If you are an innovator, your strength may lie in an intuitive visualization of connections between disparate organizational units. Know that what seems obvious to you is probably opaque to others. In the end:

Agitation without innovation means complaints without ways forward, and innovation without orchestration means ideas without impact.

Innovating in a large health system

One would think that resource-rich organizations are able to foster new ideas better than poor, cash-constrained startups.

However, it is remarkably difficult to innovate within a large health system on an ad hoc basis, for the same reason that it is difficult to innovate in a large corporation.  For one, it’s all too easy to feel like a cog in a large machine.  Fear of failure, perceived lack of reward, and a paucity of institutional support are other reasons why innovation stagnates in otherwise resource-rich organizations.

But little-fish-big-pond problems are not the only ones that plague innovation.  This phenomenon is well-recognized as part of the key reasons why disruptive innovations are notoriously difficult to launch from within a corporation.

If you feel this way, you may be an “intrapreneur.”

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6 Elements of a Data-Driven Informatics Solution (1/3)

Big Data has become a radiology buzzword  (the others: machine learning, AI, and disruptive innovation are also up there).

However, there is a real problem with using the term Big Data – it isn’t just one set of data problems.  Big Data is a conglomerate of different data challenges: volume of data, heterogeneity of data, or the velocity of data are all important dimensions.  Machine learning and internet of things are others layers superimposed on the big data problem.

Sometimes it is helpful to step back and approach data problems with a common framework, a way to think about how and which facets of data science fit in a real-life workflow in the face of an actual problem.

Below is a 6-element framework that helps me think about data-driven informatics problems. They are generally in chronological order, but they are not “steps” because you frequently will find yourself going back and redefining many things.  However, the framework helps you maintain a big-picture outlook.  The reason any sufficiently complex data problem requires a team approach. Continue reading