Artificial Intelligence (AI) is playing a bigger role in healthcare, especially in mental health where there are not enough providers. In the United States, mental health services face problems like provider shortages, social stigma, and the need for care that is easy to access. AI conversational agents, also called chatbots or virtual therapists, are showing they can help by giving evidence-based treatments through talks with patients. These agents can offer support all day and night without the limits that human providers have.
Still, how well these AI systems work depends a lot on what patients and healthcare groups think about them. Two important factors that affect whether people accept and keep using AI conversational agents are trust and how much they seem human-like, sometimes called anthropomorphism. This article looks at the role of these factors in AI use in mental health, shares research findings from recent studies, and talks about how healthcare managers and IT workers in the U.S. can bring AI conversational agents into their work.
AI conversational agents are computer programs made to talk with users using natural language processing (NLP). In mental health, these agents provide evidence-based psychotherapy—like cognitive-behavioral therapy (CBT)—by having planned talks meant to find and help with mental health problems such as anxiety, depression, or stress.
One reason people like AI agents is that they might cut down the social stigma around getting mental health help. Patients who might feel shy about seeing a human therapist could find it easier to talk to an AI in their own home. Also, AI agents help solve the problem of not having enough therapists, especially in rural or less served areas in the U.S.
Even with these benefits, not everyone fully accepts or keeps using AI conversational agents. Research shows that users think carefully before they trust these systems, and whether they keep using them depends a lot on how reliable the agent seems and how human-like it feels when talking.
Trust in AI conversational agents is very important for users, especially because mental health care is sensitive. A study by Ashish Viswanath Prakash and Saini Das from the Indian Institute of Technology Kharagpur found that trust was one of the four main reasons people start using AI mental health agents. Without trust, patients may stop using AI therapy because they worry about privacy, accuracy, or if the agent can really help.
In the U.S., patient data security is controlled by strict rules like HIPAA (Health Insurance Portability and Accountability Act). Healthcare managers must make sure AI systems follow these laws. Handling data safely builds trust among patients, providers, and those who run the systems.
Healthcare IT managers have an important job here. They need to check AI service providers carefully to understand the safety measures like encryption, data hiding, and who can access the data. Also, clearly telling patients how their information is stored and used helps build trust.
Trust is also linked to how well the AI agent works. If patients feel the AI gives good advice or therapy, they will likely keep using it. But if the agent makes mistakes or does not answer well, trust goes down fast. That is why developers must use tested therapy content and keep improving the AI algorithms.
Anthropomorphism means how much AI agents seem to have human-like features. This can include how they talk, their tone, how they show empathy, or how they respond personally. Research by Amani Alabed and colleagues at Newcastle University Business School shows anthropomorphism affects how people feel connected to AI conversational agents.
Users who see the AI as more human-like tend to feel “self-congruence,” which means they feel the AI matches their own feelings or identity. This can cause what is called self–AI integration, where people include the AI in their sense of self during therapy. This makes users feel more comfortable and helps them stick to therapy because the interaction seems natural and less robotic.
In medical places across the U.S., where patients come from many cultures and backgrounds, AI agents that look a bit human can better meet what patients expect. For example, an AI that gets cultural values, uses kind language, and changes to fit someone’s personality can lower barriers and make user engagement better.
However, there are limits. Relying too much on AI might cause risks like emotional dependence or less thinking ability, sometimes called digital dementia. It is important to balance human-like talking with clear messages that AI is just a tool, to avoid confusion or bad relationships with the technology.
Knowing these points helps healthcare leaders in the U.S. pick or design AI systems that fit their patients better. Customized AI agents can improve therapy by meeting many different needs and preferences.
Using AI conversational agents in mental healthcare can do more than make patients feel more comfortable. It can also help run medical offices better and make work smoother.
In busy mental health clinics, AI powered phone systems can reduce the workload for staff, help schedule appointments, and answer common questions without needing a person. For instance, Simbo AI offers phone services that mix AI conversation and call answering, letting office staff focus on clinical jobs and harder patient talks.
By using AI to start contact with patients, the agents can gather basic details, do first screenings, and sort cases by how urgent they are. This helps make sure clinicians’ time is used well, cuts waiting times, and helps more patients get seen.
AI tools can also help take notes and record patient answers automatically, cutting down on mistakes in manual data entry and giving clinicians more time to care for patients.
Healthcare managers can use AI agents to:
IT managers must make sure AI fits with current electronic health record (EHR) systems and follows healthcare rules. Good AI setup not only improves office work but also helps clinical work by making workflows run smoothly.
Prakash and Das’s research points out that although AI can help, there are social and ethical issues in using it in U.S. healthcare. Some key concerns are patient privacy, whether AI therapy can be trusted, and if using AI too much might reduce human contact in mental health care.
Laws and rules makers should make sure AI conversational agents are safe, work well, and follow ethical guidelines. Rules should encourage clear AI design, consent from users, and teaching patients about AI to avoid misuse or wrong hopes.
Also, cooperation between AI developers, doctors, and patients is important. Working together can help create AI therapy tools that are both good and keep respect for human value. This teamwork will be needed to grow AI use while still keeping human care central.
Mental health providers in the U.S. are using AI conversational agents more to deal with staff shortages and help patients get care. To make AI work well and keep patients using it, trust and feeling that the AI is human-like are key.
Healthcare managers should pick AI vendors who follow rules well and have clear data safety policies. They should also choose AI systems that can act human-like and match a variety of patient needs to build emotional links and help patients stick with therapy.
IT staff should focus on safe AI setups that support office work like booking appointments and sorting patients. This helps run the office better.
Finally, leaders need to think about ethical issues around AI in mental health, making sure the technology helps and does not replace important human care.
Taking care of these points will help U.S. medical practices use AI conversational agents better. This can improve access to evidence-based therapy and support patients with easier and more responsive mental health care.
Intelligent Conversational Agents are AI-based systems designed to deliver evidence-based psychotherapy by interacting with users through conversation, aiming to address mental health concerns.
They aim to mitigate social stigma associated with mental health treatment and address the demand-supply imbalance in traditional mental healthcare services.
The study identified four main themes: perceived risk, perceived benefits, trust, and perceived anthropomorphism.
The study used a qualitative netnography approach with iterative thematic analysis of publicly available user reviews of popular mental health chatbots.
There is a paucity of research focusing on determinants of adoption and use of AI-based Conversational Agents specifically in mental healthcare.
It provides a visualization of factors influencing user decisions, enabling designers to meet consumer expectations through better design choices.
Concerns such as privacy, trustworthiness, accuracy of therapy, and fear of dehumanization act as inhibitors to adoption by consumers.
Policymakers can leverage the insights to better integrate AI conversational agents into formal healthcare delivery frameworks ensuring safety and efficacy.
Anthropomorphism, or human-like qualities perceived in CAs, influences user comfort and trust, impacting willingness to engage with AI agents.
Trust is critical as it affects user reliance on AI agents for sensitive mental health issues, directly impacting acceptance and usage continuity.
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