The Trust Gap in Digital Health and AI Starts With Design — Here’s How to Close It

According to Accenture, patients are twice as likely to leave a provider over a poor experience at the front desk or with online services than over substandard medical care. At the same time, trust in digital health products remains fragile: 66% of patients have little confidence that their health system will use AI responsibly, and 58% doubt it will protect them from harm.

The gap between what technology can do and how much patients trust it won’t close on its own. I’ll break down which design decisions build that trust — and which ones quietly destroy it.

Functionality doesn’t equal trust

When the U.S. gave patients instant access to their test results through patient portals, it was a meaningful step toward patient control over their own health data — but it came with unexpected consequences.

One in four patients refreshes the portal waiting for results — and those who check most often are more likely to message their doctor afterward, even for routine tests. The problem is rarely the result itself but how it’s presented: without context, a number is hard to interpret. It turns out patients process results more easily when they see a visual scale showing where their value sits relative to the normal range — reducing the urge to go searching for additional explanations.

Access to information — or the mere existence of a feature — doesn’t automatically build trust. What does varies by product type: in telemedicine, it’s knowing who the doctor is and what to expect; in treatment platforms, it’s clear statuses and an obvious next step; in AI-driven diagnostics, it’s understanding not just what the system recommends, but why — and who is validating its conclusions.

What in a medical product’s UX pushes users away

In digital health, the user experience shapes whether a person feels in control of a situation at a moment when they’re already short on certainty. That’s why UX in a medical product can either take some of the pressure off or pile more onto it. In my experience, this most often happens in three situations:

• Design that ignores the user’s state. In digital health, it’s risky to design UX as if the person opening the product is calm, focused, and ready to figure things out. They may be anxious about getting results, nervous about voicing their problem, in pain, or under stress. If the product doesn’t account for this, even routine friction — filling out a long form, having to remember information from a previous step — can feel like additional pressure.
• Information that doesn’t lead to a clear next step. In a medical product, the user doesn’t just need information. They need an answer to three questions: where am I in the process, what does this mean, and what do I do next? When the product shows a result, a status, or a recommendation without that context, the person is left with uncertainty instead of confidence.
• Tone and wording that don’t match the user’s state. In a medical product, copy is an important part of how the user interacts with it. Language that’s too technical creates distance: the person sees the terms but doesn’t understand what they mean for them specifically. Language that’s too simplified feels superficial. Marketing-style slogans with aggressive calls to action can read as manipulation. The text on a button, the name of a status, the wording of an error message — all of it shapes whether the person feels clarity at the moment they need it most.

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UX principles that build trust in digital health

If trust is broken at the level of the user experience, that’s also where it can be built. Here are a few principles that help:

• Build a path, don’t just deliver information. The more information a person takes in, the more effort it takes them to make sense of it. The classic content model — read about your condition, then pick a service yourself — shifts the burden of interpretation onto the user. Without guidance, many people never make it as far as booking a consultation. The alternative isn’t to add more content but to guide the person through it: a symptom quiz that uses a few simple questions to help them describe what they’re experiencing, then shows a recommendation and a concrete next step.

• Be honest with the user. A product shouldn’t aim to sound as calm as possible, or as confident as possible. Too much confidence can frighten people, while too much reassurance can dull their attention to risk. You can see this effect even at the level of microcopy — the small bits of interface text on buttons, statuses, and notifications. A fall-detection alert in an older adults app illustrates this well: “Fall has been detected” triggers panic, while “Don’t worry, everything is fine” plays the situation down. The right version — “Potential fall detected — Check in to ensure everything is okay” — acknowledges the risk without overstating it and offers a clear action right away.  
• Explain how the conclusion was reached, especially when AI is involved. The user needs to understand not just the next action, but where the information comes from: what’s a fact, what’s the system’s interpretation, and what a doctor has confirmed. If an AI recommendation looks identical to verified medical information, the person may either distrust the system entirely or, conversely, take an algorithm’s guess as medical fact. Both outcomes are dangerous. UX here shouldn’t try to “force” trust in the technology — it should help the person understand the limits of what they can trust the information for, and who is responsible for the decision.

What would be a minor inconvenience in another product can affect a person’s health and clinical journey in a medical one. The reasons to trust the product and keep using it have to be built into the logic of how the interaction with the user is designed.

Photo: ismagilov, Getty Images

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