Behavioral frameworks can be powerful tools in health research: they help us see patterns, design better interventions, and ask deeper questions. But applying them isn’t always straightforward. This post offers practical guidance for researchers and designers who want to move from theory to study design, from the page to
“User-centered design” has become a default label in healthcare innovation. But what does it really mean? In many digital health projects, the term is used loosely, detached from the realities of care work, systems complexity, and patient vulnerability. This post offers a critique of how “user-centered” has been flattened into
Many health tech tools claim to “change behavior.” They offer reminders, nudges, rewards, but too often they confuse compliance with care. This post explores the difference between influencing behavior and supporting people, and asks what it would look like to design health technology that respects autonomy, context, and complexity instead
“User-centered design” has become a default label in healthcare innovation. But what does it really mean? In many digital health projects, the term is used loosely, detached from the realities of care work, systems complexity, and patient vulnerability. This post offers a critique of how “user-centered” has been flattened into
AI is quickly becoming a staple in health technology assessment. Forecasting, evidence synthesis, signal detection: all are being optimized by algorithms. But the more we automate, the more we notice what can’t be automated: trust, participation, lived experience. This fieldnote captures my reflections from the AI and Patient Experience
When we talk about AI in healthcare, we usually focus on performance. How accurate is the system? How fast? But we often skip over a more subtle question: how does it feel to use? Artificial Intelligence Experience, or AIX, is about how people experience AI systems. It’s the emotional,
Healthcare systems worldwide are undergoing transformation—digitally, organizationally, and culturally. Yet despite technological advancement and policy reform, many interventions continue to fall short of intended outcomes due to a fundamental oversight: human behavior. Behavioral science offers a framework for understanding the cognitive, social, and emotional drivers behind how individuals interact
AI is quickly becoming a staple in health technology assessment. Forecasting, evidence synthesis, signal detection: all are being optimized by algorithms. But the more we automate, the more we notice what can’t be automated: trust, participation, lived experience. This fieldnote captures my reflections from the AI and Patient Experience
When we talk about AI in healthcare, we usually focus on performance. How accurate is the system? How fast? But we often skip over a more subtle question: how does it feel to use? Artificial Intelligence Experience, or AIX, is about how people experience AI systems. It’s the emotional,
Clinical research is often built for speed: timelines are tight, protocols are rigid, and participant experiences are frequently treated as logistical hurdles. But what happens when we shift the focus from control to care, from efficiency to reflection? This post explores how slow design, a philosophy born in response to
Health Technology Assessment (HTA) relies on rigorous evidence to inform decisions about what technologies, treatments, and interventions should be funded, prioritized, or approved. Yet much of the existing evidence infrastructure is blind to behavioral drivers: the social, emotional, and cognitive patterns that shape real-world health outcomes. This article explores how
Healthcare systems worldwide are undergoing transformation—digitally, organizationally, and culturally. Yet despite technological advancement and policy reform, many interventions continue to fall short of intended outcomes due to a fundamental oversight: human behavior. Behavioral science offers a framework for understanding the cognitive, social, and emotional drivers behind how individuals interact
