Digital literacy in healthcare means that doctors, nurses, and other health workers need to understand how technology, including AI systems, works. This helps them use these tools safely and well. AI is now part of many medical tasks like diagnosing patients, planning treatments, and handling patient data. But most clinicians learn medicine and may not know much about computers, coding, or how AI works.
A five-year study led by Roanne van Voorst showed an important problem: many healthcare workers do not have enough computer skills to safely manage AI tools. This can cause issues when doctors and nurses have to interpret AI advice without fully understanding how it was made. The study included 121 doctors and nurses along with 35 ethicists and software experts from hospitals in countries like the Netherlands, China, and the United Arab Emirates. Their findings apply to the United States as well.
Medical bosses and IT managers in the U.S. must see that just adding AI technology is not enough. Clinicians need ongoing education and training in AI and digital health skills. These programs should teach both medicine and computing to help close the knowledge gap.
One big problem with using AI safely is human oversight. Doctors do not make choices all alone anymore. Instead, they combine AI suggestions with their own judgment. But this mix can bring new problems.
Overtrust and AI Fatigue: Some clinicians trust AI too much, thinking it never makes mistakes. Others get tired of seeing too many alerts from AI, especially ones that are false alarms. This “AI fatigue” can cause them to ignore warnings. Both of these situations can be bad for patient care.
In the Netherlands, a heart doctor said that AI training sessions are sometimes done just to meet requirements, since doctors are busy moving on to the next patient. This shows how tight time is at medical offices, especially in smaller U.S. clinics where there is little time for extra training.
The Black Box Problem and Explainability Limits: Many AI systems work like “black boxes.” This means it is hard to see or explain how they make decisions. Current ways to explain AI results do not fully show how the answers are given. A report in The Lancet said explanations should not replace careful testing of AI because they have limits. This makes it hard for clinicians who do not know much about computers to decide if they should trust AI advice.
When doctors are expected to check AI outputs, they might carry responsibility they are not ready for. This creates ethical and legal questions in the U.S. where malpractice is a concern. Medical leaders must set rules to make sure clinicians are not unfairly held responsible.
New technology is changing how clinicians learn and work. Older doctors often use gut feelings built from years of experience when making decisions. Younger clinicians, who grew up with many digital tools, might lack some of this intuition because they rely more on technology.
Van Voorst’s research describes a “mechanical sixth sense” that nurses develop when using tools like video monitoring for remote care. Since they can’t be with patients in person, they learn to notice small signs to make decisions. This is important in the U.S., where telehealth and remote care have increased, especially after COVID-19.
Still, clinicians must find a balance. If they trust AI too much without checking with their own judgment, mistakes can happen. But ignoring AI because it is not well understood is also a problem.
Training should cover both digital skills and good clinical reasoning. Healthcare managers need to support AI training that teaches how AI works, its limits, and how to handle risks, while keeping clinical skills strong.
AI can also help make medical office work smoother. U.S. clinics want to be more efficient, and AI automation is one way to do this. For example, Simbo AI offers automated phone answering service that handles common calls without staff help.
Front-Office Phone Automation: Medical office front desks get many calls about appointments, questions, prescription refills, and billing. This takes a lot of staff time. Simbo AI can answer routine calls, schedule appointments, and even ask some medical questions before passing calls to humans if needed.
Using AI this way can lower staff workload, reduce wait times for patients, and let workers spend more time with patients. Clinics with fewer staff or busier schedules find this useful. However, staff and managers must know how the AI works and what it cannot do, so they can handle special cases.
Clinical Workflow Support: AI tools are also part of electronic health records to help doctors with medicine management, alerts, and treatment ideas. For example, AI can warn about drug problems, suggest treatments, or point out patient risks.
But the Health-AI project found that AI sometimes adds more work instead of reducing it. Doctors can get many alerts, some not important, which leads to alert fatigue. This means they may ignore important warnings.
Good workflow design is key. Plans should include:
Medical leaders and IT teams need to work together so AI supports work rather than slows it down. Integration must fix workflow problems and make using AI easier.
Using AI in healthcare brings important ethical and policy questions. In the U.S., medical groups must balance using new technology with keeping patients safe, private, and treated fairly.
The American Nurses Association says AI should be used responsibly. This means protecting patient privacy, being fair, and being open about AI use. Training on AI is needed so clinicians can spot biases in AI and understand privacy risks. If AI is trained on biased data, it can worsen inequalities. Healthcare managers must not ignore this.
Policy makers and leaders in the U.S. should make rules to help AI be used safely. This could include:
Without good policies and education, patient safety and clinician protection might be at risk.
AI use in U.S. healthcare depends a lot on how ready the workforce is. Stephanie H. Hoelscher and Ashley Pugh suggest a plan called N.U.R.S.E.S. to help train nurses and healthcare workers for AI. It stands for:
This plan mixes school education with real-world practice to build skills in AI and ethics for nurses. Healthcare groups in the U.S. can adapt this for all clinicians to keep education ongoing and support safe AI use.
Hospital leaders and IT experts should work together to create training programs. They can run hands-on simulations, use case studies, and hold workshops that improve confidence in using AI tools.
In U.S. medical offices—from solo doctors to big centers—administrators, owners, and IT managers have key roles in AI adoption. Their tasks include:
Adding AI to patient care in the U.S. means clinicians need better digital skills. Problems like trusting AI too much, getting tired of AI alerts, and not being able to explain AI show that human oversight is complicated. Training must teach not just technology, but also ethics and clinical judgment. As clinical skills change with technology, education should keep up.
Companies like Simbo AI show how AI can help with office work by automating phone tasks. This helps clinics with many calls and fewer staff. Still, no AI tool takes the place of skilled clinicians who can understand and manage AI advice safely.
Medical practice managers, owners, and IT staff have an important role. They must focus on good workflows, training, support, and ethics to make sure AI helps care without causing harm to patients or staff.
Human oversight faces challenges like unrealistic expectations for clinicians to fully understand AI, the black-box nature of algorithms, high workload and time constraints, and the need for evolving digital literacy alongside diminishing traditional clinical intuition.
Decisions are increasingly hybrid, with AI influencing clinicians both consciously and subconsciously. Overtrust or ‘AI fatigue’ can cause clinicians either to overly rely on or ignore AI outputs, blurring autonomous human decision-making.
Usually not; clinicians lack training in computational processes. Explainability methods don’t reliably clarify individual AI decisions, and clinicians’ shallow AI understanding risks shifting responsibility unfairly from developers to users.
Risks include misassigned accountability when AI errs, burdening healthcare providers with computational skills, false security in AI decisions, and ethical concerns due to insufficient explainability and pressure on professionals under high workload.
High workload and efficiency expectations reduce time available for clinicians to verify AI outputs or pursue training, potentially leading to overreliance on AI decisions and compromised patient care quality.
Clinicians trained before AI rely on intuition and sensory skills, but newer generations spend more time on digital tools training, risking erosion of intuitive diagnosis skills crucial for contrasting AI recommendations.
Current explainability methods can’t provide reliable explanations for individual decisions, creating a façade of transparency that may mislead clinicians into false confidence rather than ensuring meaningful understanding or safety.
Besides clinical duties, providers must manage digital documentation, be vigilant for AI errors or false alarms, and engage in continuous AI-related education, adding to workload and reducing time for direct patient care.
Differences in language, error definitions, and expectations create challenges; while co-creation is beneficial, it rarely results in fully trustworthy AI without misunderstandings and mismatched priorities.
Frameworks must address clinicians’ work pressures, digital literacy limits, time constraints, explainability issues, and skillset changes, ensuring support systems that balance AI benefits with safeguarding clinician capacity and patient care ethics.
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