Artificial intelligence (AI) is slowly changing healthcare. It is used for things like patient scheduling and office work. In the United States, many medical offices are starting to use AI tools such as chatbots, virtual assistants, and phone automation. But there are still questions about how much AI can replace talking to real people, especially in healthcare. People who run medical offices need to understand how culture affects AI acceptance. They also need to know where AI cannot replace human contact.
Researchers including Sage Kelly, Sherrie-Anne Kaye, and Oscar Oviedo-Trespalacios looked at 60 studies about AI acceptance. These studies were in many industries but relate well to healthcare. They found important reasons why people want or do not want to use AI. These include perceived usefulness, trust, performance expectancy, attitude, and effort expectancy.
Perceived usefulness means users think AI will help them do tasks better or faster. Performance expectancy is the belief AI will improve work and results. Attitude is how people generally feel about AI, whether good or bad. Trust means confidence that AI works well and keeps data safe. Effort expectancy tells how easy it is to use the technology. These factors influence if people will try or keep using AI in many fields, including healthcare.
In the U.S., healthcare leaders think about these factors when using AI. For example, front-office phone systems, like those from Simbo AI, depend on these ideas. Simbo AI’s products help answer calls, route them, and schedule appointments using conversational AI. This reduces work for staff and helps patients get service.
Culture has a big effect on whether people accept AI. In places where talking to real people is important, even easy and useful AI cannot fully replace human contact. Patients in healthcare want comfort, care, and clear communication with staff. This changes how people feel about AI.
The U.S. is a diverse country where personal care and trust in healthcare workers matter a lot. Even though AI can be efficient, many patients still want a kind human voice when they call or visit. This limits how much AI can fully replace people.
Medical staff see that AI works well when it supports human communication, not replaces it. AI answering phones can handle simple questions or schedule visits during busy times. But for complicated problems or sensitive topics, patients want to speak with real humans. This help builds trust and leads to better care and satisfaction.
Also, health differences in the U.S. mean some minority groups may trust AI less because of past unfair treatment. AI that is not trained with diverse data could be biased, which might cause more distrust. Healthcare offices must think about their patients’ needs and cultural wishes when they use AI.
AI can help make healthcare work smoother without lowering care quality. AI tools can automate simple front-office tasks so staff can focus on more important jobs.
Companies like Simbo AI offer phone systems using conversational AI. These systems handle calls, manage appointments, answer general questions, and send calls to the right places. This cuts down call wait times and lets staff spend more time on work that needs human care.
AI automation lowers the work of booking and changing appointments. Instead of having staff answer every call, AI can do it anytime, reducing mistakes and delays. This is important for busy clinics.
Human staff can then check complicated cases, give visit instructions, or handle insurance questions. Patients get faster answers to simple questions. This mix of AI and human help respects U.S. cultural preferences for personal care and raises efficiency.
AI can sort calls by how urgent they are or why the patient called. Simple requests like appointment changes or insurance questions can be done by AI. More serious issues like pain or medicine side effects go to skilled staff who know about these problems.
This way, AI helps handle many calls but keeps human contact when it is important for safety and care quality.
AI also helps keep track of and document calls. This helps medical offices follow rules about records and security. Automated notes can reduce errors and improve quality control.
Practice leaders must make sure AI meets U.S. privacy laws like HIPAA. Clear policies and strong data protection build trust and support acceptance.
Healthcare leaders need to think about ethical issues of using AI. The American Nurses Association points out risks like biased AI, privacy problems, and relying too much on AI, which could reduce critical thinking by healthcare workers.
The U.S. serves many groups, including minorities and Indigenous peoples, who may be affected unfairly by biased AI. Many AI systems do not have enough data from these groups. This can cause unequal care if decisions depend too much on AI results.
To handle these problems, leaders should:
AI is being used more in healthcare, but studies say AI is not likely to replace humans in many patient roles soon. People still want human contact and trust. AI systems need to fit these needs.
The Technology Acceptance Model (TAM) is a tool that helps healthcare leaders understand and increase AI acceptance. Focusing on usefulness, easy use, and trust is important to make AI work well.
The best AI uses in U.S. medical offices mix technology with human care. Phone automation from Simbo AI shows how AI can handle routine tasks while keeping human connections where they matter.
Medical leaders, owners, and IT managers should keep up with new AI trends and cultural changes. Knowing how to balance technology and personal care will help provide efficient and respectful healthcare in the U.S.
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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