Artificial intelligence cannot improve health care if clinicians and staff are not trained to use and coordinate it

Healthcare systems are racing to roll out artificial intelligence for diagnosis, documentation, scheduling, coding and patient communication, but without workforce training, they are expeditiously exposed to new risks.

Leaders often assume that AI technologies themselves will drive improvements, but unprepared clinicians and non-clinical staff can easily misuse, distrust, over-rely on, or abandon these tools altogether.

It's the difference between buying a Ferrari and feeling confident that you know how to drive it safely at high speeds. Providing healthcare teams with powerful AI tools without training undermines their ability to safely and effectively use potentially system-changing tools.

AI readiness for more than just one-time adoption

Two-thirds of doctors now use augmented intelligence, according to the American Medical Association, but healthcare still lags behind other industries in AI adoption. The World Economic Forum reports that the main reasons are the gap between technology and strategic plans, workforce readiness and growing distrust of artificial intelligence.

In many healthcare systems, clinicians and non-clinical staff are not yet ready to use AI safely and sustainably. That’s because AI training is often viewed as a one-time requirement or a simple box to be checked, rather than an ongoing investment. Closing this gap requires role-specific learning that builds confidence and judgment over time, not just at the time of adoption.

Success with healthcare AI requires new workforce skills

AI readiness involves more than just technical skills. Healthcare teams need a new way of thinking that matches how AI actually works. By integrating artificial intelligence into the tool, it can provide best-guess predictions and recommendations based on statistical likelihood and confidence scores rather than certainty. So instead of thinking “if this, then that” we move to “if this, then this is the most likely answer”.

The goals of training should not be limited to teaching clinicians and non-clinical staff how to use AI tools, but should include how to become AI facilitators who are able to:

• Interpret output
• Question result
• recognize limitations
• Cover machine recommendations

When AI tools are deployed without this understanding, predictable failures can occur.

Clinicians may be overly reliant on AI in areas such as decision support, triage, and documentation. Or, when not fully understanding how recommendations are generated, they may apply output inconsistently, leading to failures in diagnosing, documenting, and delivering care.

Without the right training, systems can suffer from “automation bias,” where employees stop thinking critically because the AI ​​is often right, or “algorithmic disuse,” where they stop using the AI ​​after making a mistake. Good news? Both conditions are preventable with better training and guidance.

Role-specific training that matches employee responsibilities

The best training for different roles is to put people in real-life scenarios and develop clear guidelines for their use. The goal here is not only to build familiarity with AI but also confidence in judgment so that staff and clinicians understand what AI does and, just as importantly, what it does not do.

This is how AI earns its place as a trusted collaborator. It starts here:

• Leverage artificial intelligence as a support for, not a replacement for, clinical judgment: Clinicians need to know how to provide accurate input, maintain supervision, and interpret recommendations in a clinical setting. They should also be able to recognize AI’s limitations and biases and understand when their judgment is superior to AI’s recommendations. Therefore, if nurses understand why an AI system flags a patient as being at risk for sepsis, they can validate the threat based on their own assessment rather than blindly following the AI's recommended care pathway.

• Position management teams as AI contributors rather than passive users: AI training should help management teams understand when they can trust AI-generated output and how to identify and manage cases that AI and automation cannot resolve. But training should also increase the importance of their non-clinical roles. Training needs to go beyond usage proficiency so that employees understand that every record they enter into the EHR is training and informing the AI. It makes important contributions to quality of care and system intelligence.

• Establish AI as a core capability, not just a one-time rollout: For operational and clinical leaders, AI training is less about operating the tools and more about becoming stewards of the technology. Leaders must have the ability to set clear expectations for appropriate AI use and actively monitor adoption and usage patterns. When performance, trust, or reliability issues inevitably arise with AI, these leaders also need the confidence, skills, and authority to respond quickly to adjust workflows, training, and coaching as needed.

The promise of artificial intelligence to improve health care systems won’t be realized simply by purchasing more advanced tools. It depends on ongoing investment in training to ensure clinicians, staff and leaders can confidently question output, use judgment and manage risk. Leaders who consciously invest in workforce readiness will transform AI from a shiny purchase into a powerful, efficient tool.

Photo: Leo Wolfert, Getty Images

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