Artificial intelligence (AI) is becoming an important tool in many industries, including healthcare. In the United States, medical practice managers, healthcare facility owners, and IT staff are thinking about how AI can improve service, lower workloads, and make patients happier. But adopting AI is not just about having the technology available—it also depends on whether healthcare workers and managers are willing to use it. It is important to understand what affects their intention to use AI when trying to add it to healthcare work.
This article looks at how perceived usefulness, trust, and effort expectancy affect the willingness to use AI in healthcare settings in the United States. These factors come from research and apply especially to front-office automation tools like Simbo AI’s phone answering system, which uses AI to make communication in medical offices easier.
Behavioral intention means how ready or willing a person is to use a new technology. In healthcare, this willingness affects how easily AI tools fit into daily work, training needs, costs, and continued use. Research using the extended Technology Acceptance Model (TAM) shows several key factors that influence intention and actual use of AI: perceived usefulness, performance expectancy, attitudes, trust, and effort expectancy.
For healthcare managers looking at AI tools like Simbo AI’s front-office system, knowing these factors can help achieve better results, more staff acceptance, and improved patient care.
The main factor in choosing to use technology is perceived usefulness—whether people think the tool will make their work or healthcare better. For front-office healthcare tasks, this means checking if an AI answering service:
A review of 60 studies on AI acceptance showed that perceived usefulness is a strong reason people want to use AI systems. Healthcare managers and IT staff are more likely to support AI if they see clear gains in daily work.
For example, Simbo AI’s platform handles patient calls automatically, freeing receptionists to help patients in person and work on harder tasks. This shows real improvements to front-office work. When benefits like this are obvious, people’s view of usefulness grows, which leads to better attitudes toward AI.
Trust is very important in deciding whether to use AI. Healthcare offices handle sensitive patient data and need reliable communication systems. Trust means believing AI is accurate, keeps data safe, and works consistently.
Studies show trust is a strong factor in AI acceptance because users need to know the AI will work properly and not cause mistakes or leaks. In healthcare, this is critical. For example, if AI mishandles patient info or routes calls incorrectly, trust goes down and people may reject the system.
Companies like Simbo AI must be clear about how their AI works, protect data well, and explain how they use information. Trust also grows when AI supports the work staff do instead of replacing human care. When AI is easy to use, fits into workflows, and does its job reliably, users trust it more.
Effort expectancy means how easy people think a technology is to use. In busy healthcare offices, staff cannot spend a lot of time learning or fixing new tools. AI that seems hard or confusing is less likely to be accepted, even if it is useful.
Good AI front-office tools like Simbo AI’s answering service must be simple, need little training, and work well with current software. When staff find AI easy to use, effort expectancy makes them more willing to adopt it.
Clear interfaces, easy setups, and quick customer help can raise effort expectancy. This helps healthcare providers and staff add AI into their daily work with less trouble.
Research shows culture affects AI acceptance, especially because human contact is valued in healthcare. Even if AI is useful and easy, some patients and staff prefer talking to people directly. Healthcare managers must balance using AI while keeping important human elements in patient care and trust.
In the U.S., patient satisfaction affects reimbursements and reputation. AI tools should help, not replace, human interaction. For example, AI can handle routine questions and scheduling, letting staff spend more time on personal care and complex patient needs.
AI changes healthcare work by making repetitive and time-consuming tasks easier. Front-office phone automation is a good example. AI answers many calls, schedules appointments, sends reminders, and directs urgent calls to staff. This reduces the load on receptionists and office managers, so they can focus on more important work.
AI also improves accuracy by cutting human errors in call routing or scheduling. This means fewer missed calls and smoother appointment setting, helping patient experience.
For U.S. medical managers and IT staff, solutions like Simbo AI offer clear benefits such as:
AI can handle many calls at once, so patients don’t have to wait on hold for long, which is a common issue in busy offices.
Research shows gaps in AI studies, like unclear AI definitions and mostly self-reported data. This limits understanding how AI is really used. Healthcare managers should choose solutions backed by real-world data and honest user feedback, not just marketing or small tests.
Concerns about job loss and how much staff know about AI affect attitudes. Education and clear communication about AI helping, not replacing, staff can reduce worries.
To build trust, show security measures, offer trial periods for staff, and include user feedback when adapting the AI system.
Healthcare leaders are important for AI adoption. They must understand how people accept technology and lead change in their organizations.
Choosing and using AI needs thinking about staff readiness, technical systems, patient preferences, and legal rules. IT managers must ensure systems work well together and data is safe. Practice owners must balance costs and benefits of AI services.
Matching AI tools with current workflows and keeping open talks with all healthcare staff helps create a workplace ready for AI benefits without hurting essential human care.
Overall, perceived usefulness, trust, and effort expectancy are key for healthcare groups in the United States when thinking about AI technology. By focusing on these, healthcare managers can make good decisions to use AI tools like Simbo AI’s phone automation services, improving efficiency while keeping patient care quality.
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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