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For decades, healthcare has measured success using familiar indicators. ✅ How many patients were treated. ✅ How many surgeries were performed. ✅ How many beds were occupied. ✅ How quickly emergency departments responded. ✅ How many lives were saved. These are all important measures, and they always will be. But perhaps one of the greatest achievements of modern healthcare is missing from the dashboard. The patient who never needed to become an emergency. Imagine a patient wit

In the previous two editions, I explored how AI can support clinicians directly and how patient-facing AI can extend care beyond clinical walls. But there is another side of the story that receives less attention and yet has enormous impact on hospital performance: operational AI. If clinician-facing AI helps improve decision-making at the point of care, operational AI helps improve how care is delivered, coordinated, financed, and sustained behind the scenes. It influences p

#ConnectedHealthcare is not only about connecting hospitals, clinicians, and data systems. A truly connected healthcare model also extends care beyond the clinical walls, empowering individuals to better understand and manage their own health. In the previous edition, we explored clinician-facing AI—technologies that support physicians in diagnosis, treatment planning, and patient monitoring. But healthcare does not only happen in hospitals or clinics. Most of a person’s heal

In the previous edition, I introduced a functional framework for AI in healthcare and suggested that one of the most practical ways to understand this space is to look at who primarily uses the solution and where it creates value. As outlined, let's start with the most debated category: clinician-facing AI. I believe, this is the category that attracts the most skepticism. Many clinicians worry that AI is being positioned as an alternative to their expertise, or that it will

AI in healthcare is not one category of technology. It spans a wide range of solutions designed for different users, serving different purposes, and creating value in very different parts of the healthcare ecosystem. To make sense of this landscape, I’d like to walk through a structured way of looking at it. Rather than classifying AI by technical architecture or algorithm type, I find it more practical to group solutions based on who primarily uses them and where they create

When the conversation turns to artificial intelligence in healthcare, one of the most frequent concerns from clinicians and hospital leaders is the potential impact on revenue. The fear is that automation, optimization, and predictive insights could somehow replace billable work or lead to decreased patient volume. In reality, the evidence and emerging implementations tell a very different story: AI is not a revenue threat — when used responsibly and as part of a broader conn