The Technology Acceptance Model, first made to check how people accept information technology, has been changed to look at AI technologies. The extended TAM studies important things that affect how willing someone is to use AI. It works in many industries, including healthcare.
The main points the model looks at are:
Among these, perceived usefulness and trust have a strong effect on whether people choose to use AI. This is backed by a review that looked at almost 8,000 articles and focused on 60 of them. The extended TAM helped explain how users act when deciding to accept AI.
Healthcare in the US has special challenges for using AI. People who manage medical offices and healthcare owners want to know how AI fits into their work without hurting human relationships, like those between patients and providers. In industries, AI is used to control production and handle customer service. These jobs face similar acceptance challenges as healthcare AI.
The review found that in many cultures, the need for human contact limits how much AI is accepted. This is very true in US healthcare, where people want personal interaction in care and trust. Even if AI is useful or easy to use, healthcare workers and patients may not fully accept it if it reduces personal contact.
Still, AI has clear benefits in US healthcare. For example, AI can do repetitive front-office jobs like scheduling appointments, checking insurance, and answering patient calls. This helps reduce the workload and lets staff spend more time with patients.
The extended TAM shows that perceived usefulness is a good predictor of whether someone wants to use AI. Hospital managers and healthcare IT staff need to see that AI clearly makes work better and improves results in order to accept it. For instance, AI answering services that cut down on wait times and automatically handle common questions prove their usefulness.
Trust is also very important. People must feel safe about data security, accuracy, and ethical AI use. This is very important in healthcare, where patient privacy and rules must be followed. Being open about how AI works and showing it helps human workers, not replace them, builds trust.
Effort expectancy means that if AI is easy to use, people are more likely to accept it. Medical offices in the US need AI front-office tools that are simple to learn and fit smoothly into daily work. If AI is hard or complicated, people avoid it and slower adoption happens.
Finally, attitudes about AI come from past experiences or workplace culture. Healthcare managers who encourage trying new things and provide training see better attitudes toward AI tools.
AI helps automate healthcare workflows, giving real benefits. Front-office tasks like patient registration, handling appointments, and answering phones take up a lot of staff time. AI can deal with routine questions and administrative tasks well and without stopping.
For example, Simbo AI focuses on AI phone automation for medical offices. Their tools can take calls, confirm appointments, and remind patients. This lowers the load on receptionists and call agents. Automating these repeated jobs makes healthcare run smoother and helps patients feel better cared for.
In healthcare management, AI also helps reduce mistakes in scheduling and billing questions. These AI systems work all day and night, answering patient calls right away. This makes patients happier and reduces missed appointments. The ability to connect easily with phone systems and electronic health records helps AI acceptance.
In US industries, AI also helps with inventory control, quality checks, and customer service. Here, AI makes complex tasks easier and raises productivity. These uses share similar acceptance reasons found by the extended TAM.
Even though usefulness, trust, and ease of use matter a lot, some cultural and mental factors slow AI acceptance in healthcare and industry. Many studies say AI might never fully replace human contact, especially where people want personal care.
Job security worries are another problem. Some healthcare workers see AI automation as a threat to their jobs. This can lead to resistance. Leaders need to talk openly and explain that AI is there to help workers, not take their jobs.
Also, not knowing much about AI lowers acceptance. Many people in studies did not clearly understand AI, which led to fear or wrong ideas. Healthcare managers should teach their teams what AI can and cannot do to build better trust and attitudes.
One more thing is that studies often use self-reported answers about AI acceptance. This is a limit. More real-life observation research would give a better idea of how people really use AI and what stops adoption in US healthcare.
Based on the extended TAM and related studies, healthcare leaders and IT managers in the US should consider these points when bringing in AI:
Understanding how people accept AI is key to using AI well in US healthcare and industry. The extended Technology Acceptance Model offers a helpful way to see what affects AI adoption, such as usefulness, trust, feelings, and ease of use.
Medical administrators, healthcare owners, and IT staff can use this information to pick and manage AI tools like front-office phone automation that fit current work routines. Clear communication, simple use, and keeping personal contact help build acceptance.
As AI grows in healthcare and industry, more studies using real-world methods will help improve how AI is used. By dealing with cultural, mental, and practical issues, healthcare groups in the US can use AI better, increase productivity, and make patient care smoother in a more tech-focused world.
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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