Researchers are discovering that patients abandon online healthcare tools not because the technology fails, but because they doubt whether AI can deliver the quality of care they expect. Jinglu Jiang, an assistant professor of management information systems at Binghamton University's School of Management, has spent years investigating the hidden reasons behind human-AI interactions in healthcare, revealing insights that could reshape how digital health platforms are designed.

What Makes Patients Distrust AI Healthcare Tools?

When patients use online medical consultation platforms or health-tracking apps, they're not just interacting with software; they're making a judgment call about whether to trust their health to an intelligent agent. Jiang's recent research, published in Information Systems Frontiers, identified a critical gap: many patients who try online healthcare services eventually stop using them because they don't believe they'll receive the best possible care from either a human or technological perspective.

This finding challenges a common assumption in digital health. The problem isn't that the technology is broken or difficult to use. Instead, patients are making a rational decision based on their perception of care quality. Some never try online healthcare at all, while others start using it and then abandon it after realizing it doesn't meet their expectations.

"My primary goal is to create or expand my own theory for human-agent interaction," said Jinglu Jiang, assistant professor of management information systems at Binghamton University.

Jinglu Jiang, Assistant Professor of Management Information Systems, Binghamton University

Jiang's approach draws on theories from sociology and psychology to understand what happens when humans and AI systems interact. Rather than treating healthcare apps as isolated technical problems, she examines the motivational factors and behavioral patterns that shape whether people trust and continue using these tools.

How Can Healthcare Designers Build Better AI Systems?

• Address Trust Gaps: Design systems that explicitly communicate how AI complements rather than replaces human judgment, helping patients understand the boundaries and strengths of automated healthcare tools.
• Support Self-Management: Create IT-based interventions that help patients manage chronic diseases by facilitating different stages of care, from monitoring to decision-making, based on theoretical frameworks that explain how technology can support each phase.
• Test Real-World Scenarios: Conduct research on actual patient populations to understand not just initial adoption, but why people continue or discontinue using online healthcare services over time.

Jiang's theoretical framework, published in MIS Quarterly, proposes how information technology can facilitate various stages of chronic disease self-management. This framework offers a roadmap for designing interventions that could increase patient adherence to long-term care plans. Rather than assuming patients will use whatever technology is available, the research suggests that successful digital health tools must be built around how patients actually think about their own care.

Why Does This Matter for AI Research Beyond Healthcare?

Jiang's work extends beyond medical apps. She investigates how people interact with intelligent agents like Alexa and Siri, exploring why someone might rely on their Fitbit for health tracking or use voice assistants to set medication reminders. These everyday interactions reveal patterns about human trust, autonomy, and how AI systems influence behavior at both individual and team levels.

Her research has been published across multiple high-impact venues, including MIT Sloan Management Review, Information Processing and Management, and the Journal of Medical Internet Research, indicating that her findings resonate across business, technology, and healthcare communities.

The broader implication is clear: as AI systems become more integrated into critical domains like healthcare, understanding the human side of the equation is just as important as optimizing the algorithms. Jiang's research suggests that the next generation of AI tools will succeed not by being more powerful, but by being designed with a deeper understanding of why people trust or distrust the systems they interact with every day.