Understanding the Complex Dynamics of Trust Building in Human-AI Interactions: Methodological Approaches and Practical Implications for Healthcare Administrators

In the United States, healthcare administrators, medical practice owners, and IT managers face increasing pressure to adopt artificial intelligence (AI) technology to improve operational efficiency, patient experience, and clinical decision-making.

One of the key challenges in integrating AI within healthcare settings is establishing trust among patients and staff toward AI-driven systems. Without a stable foundation of trust, the acceptance and effective use of AI tools remain limited, potentially hindering improvements in care quality and administrative workflows.

This article examines recent research on trust-building dynamics between human users and healthcare AI agents, highlighting findings that challenge traditional assumptions about factors influencing trust. The detailed understanding of these dynamics offers practical guidance for healthcare administrators aiming to effectively implement AI while maintaining patient and provider confidence. It also connects trust to vital technological elements such as automated front-office phone solutions, an area where companies like Simbo AI provide valuable innovations.

The Nature of Trust in Healthcare AI

Trust is a complex idea that affects whether users will accept recommendations or decisions made by AI systems. In healthcare—a place with high risks and many complex human relationships—trust is even more important. The work by Rosemary Tufon, in her dissertation at Kennesaw State University, offers a clear way to understand how trust is formed in AI-supported healthcare.

Tufon’s research model focuses on institutional trust factors. These include:

• Situational Normality: How much the setting where the AI tool works feels normal and steady to users.
• Structural Assurance: The belief that legal, technical, and organizational protections are in place to make AI use safe and reliable.
• Cognitive Reputation: How users see the healthcare organization’s honesty, reliability, and skill.

These factors make up the main beliefs that lead people to trust healthcare AI systems. They affect how ready users are to follow AI’s suggestions.

Healthcare Professional Oversight and Trust

Tufon’s study found that healthcare professional oversight, called the Human-in-the-Loop (HITL) model, does not have a big effect on trusting beliefs in AI. This goes against the common idea that human supervision is necessary to build trust.

For healthcare administrators, this is important because it suggests that just having human oversight isn’t enough to make people trust AI. Rather, strong institutional safeguards and clear policies matter more. This means administrators should focus on solid protections and openness instead of adding extra human checks that might cost more money or make things harder.

Disease Severity and AI Recommendation Acceptance

Disease severity affects how willing patients are to accept AI advice, but it does not affect trust directly. Tufon’s research shows that disease severity does not change how trust links to intention, but it still affects whether patients accept AI’s recommendations. This is useful for administrators who deal with patients having different risk levels.

For instance, patients with serious conditions might need different ways of communication to help them accept AI tools. Even if trust is the same across diseases, acceptance may vary. Administrators should know that trusting AI depends not just on how good the system is but also on how serious the patient’s condition is.

Methodological Approach Used in Trust Research

Tufon’s conclusions are strong because of the careful use of Partial Least Squares Structural Equation Modeling (PLS-SEM). This statistical method helps study complicated connections between many variables. Data were collected through a large online survey of adults in the U.S. aged 18 and older. This makes the results useful for many kinds of people and healthcare settings in the country.

This strong method gives confidence to apply the findings to healthcare in the United States where patient groups and healthcare places vary a lot.

Institutional Trust as a Cornerstone in Healthcare AI

Hospital administrators and health IT managers often focus on technical quality and easy-to-use design when adding AI tools. But this research suggests shifting the focus to building institutional trust.

Specifically:

• Situational Normality means AI tools should fit into daily work smoothly to seem familiar and steady. For example, if an AI phone answering system works well during normal office hours, patients see it as reliable and usual.
• Structural Assurance means having clear rules and processes, strong cybersecurity, and following legal and ethical standards. Providers need to show that AI systems are protected to keep patient data safe and work correctly.
• Cognitive Reputation is about the healthcare organization’s reputation. A well-known hospital or practice makes people trust AI tools more. Administrators should work to keep their organization’s good name by sharing information openly and showing commitment to quality care. This helps build confidence in AI technology.

AI and Workflow Integration in Healthcare Settings

AI can improve operations directly by automating front-office phone services. Companies like Simbo AI focus on helping medical offices by automating calls, scheduling, and triage.

AI in workflow automation helps build trust in several ways:

• Consistency in Patient Interaction: Automated phone systems give steady, predictable answers. This reduces the chance of mistakes that can happen with human receptionists. When information is reliable, patients trust the communication system more.
• Extended Access and Immediate Response: Patients want quick answers. AI phone systems work 24/7 and handle simple requests even when offices are closed. This makes services seem always available and steady.
• Operational Efficiency and Reduced Errors: Automation cuts down wait times and missed calls, improving patient experience. It also lowers the chance of human mistakes, which helps patients feel the system is safe and trustworthy.
• Data Security and Confidentiality: Good AI systems have strong security to protect patient information. This helps meet laws like HIPAA. Protecting patient privacy is key to building trust in institutions.
• Reduced Staff Burden: By handling repetitive calls, AI lets office staff spend more time on complex or personal patient needs. This improves service without losing efficiency.

Healthcare administrators and IT managers who choose AI phone systems like Simbo AI improve not only workflow but also the trust needed for AI to work well.

Practical Implications for Healthcare Administrators

Healthcare organizations in the U.S. work in a complex environment with high expectations for good and ethical patient care. Rosemary Tufon’s research gives healthcare leaders practical advice for using AI:

• Clear communication about institutional safeguards is important. Patients and staff must know about quality controls, data protection rules, and the institution’s commitment to ethical AI use.
• Building and keeping a good organizational reputation is important for making patients trust AI systems. This requires ongoing efforts to be open and reliable in all services, including those using AI.
• The Human-in-the-Loop model, useful for some AI tasks, should not be the only way to build trust. It may be better to focus on strong institutional protections and making sure AI tools fit well with operations.
• Knowing how disease severity affects patient acceptance of AI helps providers adjust communication and education. They may need to offer more help or different methods for patients with serious health issues.
• Adding AI front-office phone automation can support trust by providing steady, efficient, and safe communication. This improves operations and patient confidence together.

The Role of AI in Healthcare Administration Workflow

Besides phone automation, AI helps many parts of healthcare workflows:

• Appointment Scheduling: AI chatbots and systems manage large numbers of requests for appointments, cancellations, and changes. This frees up staff time.
• Patient Triage and Workflow Prioritization: AI helps assess initial symptoms and sort patient needs before doctor visits. This helps use provider time and resources better.
• Billing and Coding Automation: AI can catch errors in billing and improve money management for the practice.
• Clinical Decision Support: AI working with Electronic Health Records (EHR) gives doctors evidence-based advice and alerts them about drug interactions or important test results.

Healthcare leaders managing complex operations—from small clinics to big hospitals—need to pay attention not only to how well AI tools work technically but also to trust factors. This helps staff and patients accept AI and lowers resistance.

Summary of Research-Based Recommendations for U.S. Healthcare Organizations

• Focus on building institutional trust through openness, clear communication, and clear rules about AI use.
• Know that human oversight alone won’t build trust; strong institutional safeguards are more important.
• Address patient factors like disease severity when communicating about AI healthcare services.
• Use AI automations in workflows—such as phone answering, scheduling, and triage—in ways that make patient experiences steady and reliable.
• Keep checking AI systems to make sure they follow rules, work well, and match institutional values.

By following these ideas, healthcare administrators, practice owners, and IT managers in the U.S. can adopt AI tools with more confidence. This can help improve efficiency and patient satisfaction while keeping trust strong.