The following is a guest article by Yuliia Apanasenko, СЕО at Phenomenon Studio
According to Accenture, one in five patients in the U.S. switched their healthcare provider — and in nine out of ten cases, the reason had nothing to do with treatment itself. It was the experience of using the service. AI has only added to the pressure: 51% of patients say it actually reduces their trust in healthcare.
Trust in digital health is not lost through one major mistake or new technology — it is lost at the level of user experience (UX), when a person does not understand where to start or what to do next. Below, I outline the design decisions that help build it.
First Contact: From Uncertainty to Action
Imagine a person who is anxious about their symptoms opening a medical platform for the first time. After signing up, they land on a homepage filled with dozens of interface elements and descriptions of the platform’s capabilities, but not a single answer to the only question that truly matters to them: “What should I do right now?”
The “provide maximum information before action” logic fails when a person lacks the mental capacity to process that information. UX at the first touchpoint should reduce uncertainty and guide users toward the next step. Otherwise, they may complete registration but never feel confident that the service can actually help them. Several UX approaches can address this challenge:
- Guided Pathways: Lead users from symptom description to the most relevant next step through a quiz or an initial intake form, rather than forcing them to choose a medical specialty on their own
- Contextual Explanations Instead of Lengthy Disclaimers: When the service asks about symptoms or other sensitive information, immediately explain why the data is needed and how it will affect the next step in the process
- Progressive Disclosure of Sensitive Topics: An anxious user is rarely ready to share deeply personal details right away; user flows should not begin with the most intimate questions— instead, they should start with simpler, less sensitive topics
All three approaches share the same principle: guide users instead of leaving them to figure everything out on their own. To illustrate, consider the example of a men’s health clinic platform built around content: articles about potential causes of symptoms, descriptions of medical departments, and lists of services or lab tests. User behavior research often reveals that instead of diving into the materials, users simply postpone their decision to visit. However, when that wall of information is replaced with a quiz that provides a clear recommendation — such as booking an appointment with a specific specialist or taking a particular test — the product finally resolves the uncertainty the user started with: a clear next step.
Illustration by Yuliia Apanasenko
Next Stage: Long-Term Interaction
After the first contact, a person moves into long-term interaction — patient portals, treatment management platforms, remote monitoring services, and apps for people with chronic conditions. To trust them, users need to understand what is happening and what comes next. The risk of a wrong decision here is extremely high.
In a patient portal, numerical indicators can be difficult to interpret — users either overestimate the risk and unnecessarily contact a doctor, or underestimate the situation. In a study where patients reviewed laboratory results and decided what they would do in real life, 65% underestimated the need to act at least once.
Any digital health product designed for long-term interaction should not simply accumulate data, but help users understand what that data means. This requires:
- Answering the Question “What Should I Do Next?”: The patient should see not only a data point, but clear guidance on what it means: is this within normal range; should they contact a doctor, or wait? — support this with visual scales, explanations of deviations, and short contextual tips alongside the data
- Prioritizing Information: In a long-term process, users easily get lost in overload; that is why it is important to highlight the statuses that require attention right now
- Maintaining a Sense of Control: Users need to understand what data the system sees, who has access to it, and what happens in an emergency — especially in remote monitoring, elderly care support, and chronic condition apps
Illustration by Yuliia Apanasenko
Trust in AI in a Clinical Context
People tend to accept AI in administrative processes, but once it is used in clinical decision-making, trust drops noticeably. According to the Philips Future Health Index 2025, 79% of healthcare professionals believe AI can improve treatment outcomes, while only 59% of patients share that optimism.
Users interacting with AI face a fundamental question: “Why should I trust this conclusion?” They need to understand whether the AI’s conclusion aligns with a doctor’s opinion and who is responsible if the system is wrong.
Research points to where the solutions lie. A systematic review of trust factors in digital healthcare identifies several key drivers, including the degree of human interaction in automated interventions, perceived risks, and data accuracy. At the UX level, two approaches are particularly important:
- Explainability: Users need to understand the basis of an AI-generated conclusion; products can provide a brief justification alongside the recommendation, such as: “based on your symptom responses and the results of test X”
- Clearly Defined AI Role. An AI-generated recommendation should never look identical to a physician-validated conclusion; its status should be explicitly communicated: this is a preliminary recommendation that will be reviewed by a healthcare professional, while responsibility for the final decision remains with a human expert— this can be supported by both a status indicator and carefully calibrated language
In healthcare, UX design determines more than just whether a service feels convenient to use. It can directly influence whether a person receives the care they need at a critical moment.
About Yuliia Apanasenko
Yuliia Apanasenko is the Chief Executive Officer at Phenomenon Studio, a strategic design and development partner in building complex digital products. Yuliia’s expertise sits at the intersection of UX, business operations, and delivery governance. She focuses on building systems that improve the quality of digital products and creating UX solutions that build user trust and support long-term business resilience.
About Joyal Bennison