To understand why medical workers and administrators use AI systems, it is important to look at how they see these technologies. Acceptance means the willingness to use, buy, or try AI products or services.
A study that looked at 60 research papers on AI acceptance in many fields, including healthcare, found five main factors that shape how people feel about AI:
These factors not only predict if people want to use AI but also if they actually use it.
One of the most important factors is perceived usefulness. This means believing that AI systems will make work better, faster, or offer clear benefits to staff and the organization.
For medical practice administrators and owners in the U.S., AI tools like phone automation, appointment scheduling, and patient information management should save time, cut errors, or improve patient experience. These benefits affect whether staff want to use AI tools.
A recent study focused on community pharmacists in Indonesia supports this idea and can be useful for healthcare providers in the U.S. It found that pharmacists’ confidence in technology made them see AI as more useful. This link between confidence and feeling AI is helpful shows the value of training programs to make staff more comfortable with AI.
Trust is also very important. In U.S. healthcare, keeping information private, accurate, and trustworthy matters a lot. If users don’t trust AI systems because of privacy, errors, or unclear information, they will not accept the technology.
The pharmacist survey showed that trust connects positive feelings about AI with actually using it. This means even if people see AI’s benefits, without trust, they may not adopt it.
IT managers and owners should pick AI systems that have strong security, clear rules on data use, and open ways of working. Explaining how AI helps staff instead of replacing them can build trust and reduce worries about jobs.
Effort expectancy means how easy people think it is to use technology. It also affects if healthcare workers will use AI tools. Technology that is simple and comes with clear instructions usually gets used more.
The pharmacist study found that confidence in using technology helped people see AI as easy to use. This boosted their intention to try AI chatbots. For U.S. healthcare offices, this means systems should be easy for staff and come with proper training.
Making AI easy to learn and use can help staff accept new tools and reduce their resistance.
The review found that some cultures really value human contact and may resist AI replacing it.
In the U.S., patients and staff often want personal interaction and trust human decisions. AI tools like phone answering services can help with routine tasks, but technologies that replace human care or decision-making might face pushback.
Medical practice owners should balance AI use so important human contact stays and respects these cultural preferences.
Optimizing Front-Office Operations through AI
AI tools that automate front-office tasks, such as phone answering and appointment confirmations, help reduce errors, lower workloads, and speed up communication.
In U.S. medical offices where staff answer many calls, AI automation can improve patient satisfaction by reducing wait times and dropped calls. This frees staff to do tasks that need human judgment.
Bridging Technology and Human Interaction
Even with automation, good AI systems keep a balance. For example, if an AI phone system finds an urgent or complex call, it passes it to a human. This keeps the important human touch while using AI for simple tasks.
Healthcare administrators should know how to use AI tools that boost productivity but keep personal communication.
Research shows that confidence in using AI (self-efficacy) helps people adopt it.
Medical practices in the U.S. can set up training to improve staff skills with AI tools. Training makes systems easier to use and shows staff how AI helps their work.
IT managers should offer ongoing support, listen to user feedback, and improve AI systems. This builds trust and good attitudes toward AI over time.
Even though AI use is growing, many studies rely on self-reported data, which may not fully reflect actual use.
For healthcare administrators, this means they should look at real-world use, not just surveys, when making AI decisions.
Studies suggest watching AI use in real clinics to better understand how people actually use and feel about AI. This helps find problems users may not report.
The choice to use AI in healthcare depends on both psychological and technical factors. Feeling AI is useful, trusting it, and finding it easy to use are top reasons people accept and use AI.
Medical administrators and IT managers in U.S. healthcare should focus on these things to make AI work well. Training staff, using clear and reliable systems, and keeping human contact alongside AI are key.
As AI improves, practices that add these tools carefully into their daily work are more likely to run better and give better care, while keeping their staff confident and willing to work with new technology.
The review focused on user acceptance of artificial intelligence (AI) technology across multiple industries, investigating behavioral intention or willingness to use, buy, or try AI-based goods or services.
A total of 60 articles were included in the review after screening 7912 articles from multiple databases.
The extended Technology Acceptance Model (TAM) was the most frequently employed theory for evaluating user acceptance of AI technologies.
Perceived usefulness, performance expectancy, attitudes, trust, and effort expectancy were significant positive predictors of behavioral intention, willingness, and use of AI.
Yes, in some cultural situations, the intrinsic need for human contact could not be replaced or replicated by AI, regardless of its perceived usefulness or ease of use.
There is a lack of systematic synthesis and definition of AI in studies, and most rely on self-reported data, limiting understanding of actual AI technology adoption.
Future studies should use naturalistic methods to validate theoretical models predicting AI adoption and examine biases such as job security concerns and pre-existing knowledge influencing user intentions.
Acceptance is defined as the behavioral intention or willingness to use, buy, or try an AI good or service.
Only 22 out of the 60 studies defined AI for their participants; 38 studies did not provide a definition.
The acceptance factors applied across multiple industries, though the article does not specify particular sectors but implies broad applicability in personal, industrial, and social contexts.
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