Editorial
As a healthcare professional, I've witnessed the rapid changes in our field firsthand. Lately, the conversation has revolved around AI and its role in healthcare.
I've spent the last year speaking with various health systems, from small clinics to large hospital networks, all grappling with this transformation. What I've seen can be summarized in a few key points and observations that I believe are fundamental to the future of healthcare.
The Secret Cyborgs Are Here
It's quite simple: healthcare professionals are already using AI. Many of you may not realize it or be hesitant to admit it. It's been the secret weapon of many doctors, nurses and administrators. Early research indicates that many knowledge workers use AI daily, but most keep it a secret. The underlying reason? They've found that AI provides solutions, making it harder to return to older methods.
This is a considerable challenge for healthcare systems. Without open communication, new efficiencies and best practices can’t be shared, creating pockets of knowledge that fail to impact the system. We must move toward open dialogue, sharing and best practices to enable the true potential of AI to be realized throughout the health ecosystem.
Real-World Example: Dr. Ramirez, a cardiologist, quietly began using an AI tool to analyze EKG reports. He found it could identify subtle anomalies earlier than traditional methods. He kept it to himself until a hospital-wide program emerged, as his team feared that the technology might be restricted and they didn’t want to lose the benefit.
Related Article: 5 Levels of AI in Healthcare: From Chatbots to Scaled Innovation
Leadership Is the Foundation for Adoption
Leadership is not just important but also a necessity for AI adoption. The people that use AI need to be encouraged, not hindered. I've seen firsthand how critical it is for leaders to set the tone: AI is not a threat but an avenue for improvement. They need to articulate a clear vision, showing how AI will enhance, not replace, the roles of healthcare professionals.
Many of us are concerned that AI might undermine our jobs. The responsibility of leaders is to build a future vision where AI and healthcare professionals work side by side to improve healthcare.
Furthermore, the most efficient organizations have created AI-dedicated units. These units are designed to understand how AI interacts with all departments and provide the support to make them all work efficiently. They also have direct guidance from the top of the company. It’s a clear message that AI is a top priority.
Real-World Example: A hospital CEO started personally using AI for scheduling and patient communication. He became a champion for the technology, showing his team how it was helping him, which opened up a system-wide initiative. This was a clear message for the staff.
Experimentation Is Still the Name of the Game
It was nice to think that 2024 was a year of experiments and that in 2025, we would see a return on investment in the use of AI. But what we saw was more of the same.
Many teams have been fumbling and trying different things. One must think of it as a continuation of experiments, a period of iterations and tests. I believe this is because AI is so radically different. We are in a learning phase, understanding that our limitations are related to how we are using the tools, not the tools themselves. A lot of trials and errors are necessary, and that is ok. If you haven’t found clear returns on investment, don’t let it hamper you.
Real-World Example: A community clinic tried three different AI-driven patient scheduling systems, finding that each had its advantages. After a period of assessment and tests, they selected the one with the biggest impact on their workflow. This trial and error process, not an immediate result, moved the clinic forward.
Pilot Programs Have Their Limits
Many health systems have several pilot programs that haven't gone anywhere. These pilot programs have shown promise but failed to expand beyond the pilot phase. To make the adoption of AI easier, there is an urgent need to develop the means and tools to support them. We need to understand how our employees work today, how they can use AI as a tool, track the experiments and then scale the successful models.
This involves a change on multiple fronts: performance assessment, learning and development and managing change. This is an ongoing transformation, not a one-time change. The systems we design today to manage change are the systems we will use tomorrow. Internal teams must drive this process, with external consulting to help us move. The applications of AI are so unique to each organization that there can’t be a complete outsourcing.
Real-World Example: A large hospital system had several pilot programs running but struggled to scale them. They developed a team to track, test and implement the most efficient solutions and develop the knowledge in-house.
Confidence Is Growing, Not in Tools, but in Application
It is clear that health organizations are growing in confidence. They are starting to understand how AI can be applied to improve their specific needs. Many organizations are developing custom software to meet their needs.
That being said, third-party providers will be essential because of their high level of specialization. The important thing to note here is the willingness to try new things and to implement solutions. In this case, understanding the locus of change.
Real-World Example: A rural healthcare network built its own AI-powered platform to manage patient data and improve its telehealth capabilities instead of buying an off-the-shelf product. It was specific to their needs and provided very good results.
Models Are Not the End All
The focus is not on the model itself. The best-performing organizations understand that the technology we choose is not the determining factor. It's all about how AI is integrated into systems, not the model itself. In other words, what's really important is creating the right conditions for using the technology.
Real-World Example: Two hospitals adopted different AI models for diagnostic imaging. The one that thrived was the one that created a new system that trained its staff on using AI effectively. It was not the technology but the system itself.
Related Article: 5 AI Case Studies in Health Care
Infrastructure Is Necessary
Many organizations spent 2024 building the necessary infrastructure, not just the physical infrastructure but also systems for training and using the new technology.
Organizations are also taking data more seriously. Data readiness has become critical for getting the most out of AI. This is important, as the way we use our data is related to the evolution of the technology itself.
Real-World Example: A network of specialty clinics focused on organizing and cleaning patient data, making sure the information could be used with the latest AI models for treatment analysis. This gave them a big advantage over the competition.
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Buy-In Is the Answer
When implementing AI, buy-in from all stakeholders is necessary. The companies that involve all the different departments from legal to security are the ones that thrive. There is so much enthusiasm for the use of AI that many people are willing to become internal advocates, instead of barriers.
Real-World Example: A hospital system included all departments in the AI selection process. The legal, compliance and security teams became internal advocates and supported the hospital's transition.
Speed Is Relative
When it comes to AI implementation, there is no such thing as fast enough. The companies that are more advanced, are the ones that feel that they are lagging behind. That’s because this is like an iceberg, as we explore a new possibility, we can see many more below it.
Organizations need to benchmark themselves in two different ways, against the competition and against themselves. Those that focus on empowering the staff and improving internal systems will outperform the rest. The specific impact of AI is so unique, that it requires internal experimentation to see how it affects each institution.
Real-World Example: A clinic focused more on improving its processes and staff training, instead of comparing itself with other clinics. It developed a fast and effective way of implementing the latest technologies.
Don’t Slow Down
There has been a slowdown in the scaling of some AI models. However, that is not a reason to slow down your AI strategy. If AI development stopped today, it would still take a decade to understand how it affects us. So, any slowdown is a chance to catch up, not to rest. With the vertical applications coming online, it will feel like the technology is still advancing at a great pace.
Real-World Example: A pharmaceutical company didn't slow its AI adoption strategy, even with the news of a slowdown in AI scaling. Instead, it used this time to learn and adapt the technology to its processes. This allowed it to be ahead of the competition.
Go Beyond One-to-One Replacement
We are now in a stage where the focus is the one-to-one replacement. For example, AI can do some of our work, faster and cheaper. But the organizations that thrive will be the ones that use AI to innovate, not just to replace. The ones that go beyond the one-to-one implementation of current functions.
Real-World Example: Instead of just having AI schedule appointments, a medical practice started using AI to identify people at risk and proactively offer them care. It was not about doing the same task, but about doing a new task.
Watch Out for Agents
The agents are here. Initially, we will see them as one-to-one replacements. Customer service bots, for example, may be used to replace customer service employees. But that will only be the start.
Imagine a manager with an army of AI-powered assistants. What possibilities would this open? That is where the transformation will truly happen. And when it comes to the agents, we are back in the pilot phase, with lots of experiments to be done.
Real-World Example: A hospital system is experimenting with an AI agent to manage patient follow-up care and provide home advice. It is still in the pilot phase, but the possibilities are vast.
Mindset Is Key
Mindset is more important than anything else. Leadership needs to foster a culture of change, where AI is a continuous evolution, not a one-time transformation. Organizations must be designed to adapt and change. We must create systems that embrace change as a new norm.
Real-World Example: A health organization developed a program to encourage and support innovation, helping to create a mindset that embraced change. It was a game-changer for them.
Related Article: Do's, Don'ts and Must-Haves for Agentic AI
Opportunity Is the Goal, Not Efficiency
AI is an opportunity to be seized, not just a technology that is designed to increase efficiency. The true winners will be the organizations that use AI to create more, instead of doing more with less.
What if a doctor had an army of AI agents? What if patient care was always available with an outstanding experience? What if the health system could address complex challenges in ways not possible before? It’s all within our grasp.
Real-World Example: Instead of just using AI to cut costs, a hospital system used AI to improve the patient experience with a 24/7 concierge. It is a win-win for the institution and patients.
The Road Ahead for AI and Healthcare
I'm impressed by how quickly health organizations have started adopting AI. However, tool developers are still trying to catch up. AI is pushing us to new limits, and I am excited to participate in this transformation. The road ahead will not be easy, but it will be extraordinary.
Frequently Asked Questions
How can healthcare professionals overcome hesitancy to use AI?
By fostering a safe learning environment with training, real-world examples and emphasizing AI as an assistant, not a replacement. Peer-to-peer sharing of successes and transparency about AI's capabilities are also critical.
What organizational structures are key for AI adoption, beyond just buying software?
Prioritize a multidisciplinary team to assess needs, create a strong data strategy and change management process, develop internal frameworks for tracking and scaling and establish continuous learning programs.
What's the biggest mistake when adopting AI, and how can it be avoided?
The biggest mistake is focusing too much on technology and not enough on the necessary supporting systems. Avoid it by prioritizing the human and operational infrastructure first, along with a clear understanding of existing workflows.
If ROI is hard to track early on, what metrics should leaders focus on?
Focus on efficiency improvements, enhanced patient outcomes, improved staff satisfaction and innovation metrics, as these demonstrate value and create a base for later ROI measures.
How can organizations ensure ethical AI use?
By creating diverse teams, implementing robust data governance, conducting regular bias audits, being transparent about the AI tools and establishing patient consent and feedback mechanisms.
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