Digital health platforms are improving access, prevention, and continuity of care, but communication remains a key challenge. If an AI tool doesn’t sound like the person using it, engagement suffers. Joe Kiani, Masimo and Willow Laboratories founder, understands this. His latest innovation, Nutu™, centers on making health guidance feel natural and human. By speaking the patient’s language, Nutu delivers science-backed prompts in a way that’s clear, supportive, and easy to connect with, because in personal healthcare, how something is said matters as much as what is said.
Human-centered design is ultimately about trust. When people feel understood, engagement improves, and follow-through becomes more consistent. In digital health, the promise of AI is practical guidance delivered with clarity at the right moment. That depends on choices about language, timing, and context that make support feel relevant to daily life.
The Problem with One-Size-Fits-All Messaging
Many digital platforms use generic messaging. “Get moving!” “Eat better!” “Track your sleep!” While accurate, these messages often miss the mark. They assume that users are motivated the same way, respond to the same tone, and understand health in the same terms. But a person managing prediabetes while working two jobs may not respond to the same language as someone preparing for a marathon. A caregiver managing stress might need a different tone than a college student focused on sleep quality. That’s why AI tools need to adjust, not just what they say, but how they say it.
Behavior-Based Personalization
AI starts learning as soon as someone logs in. It doesn’t just track steps or meals. It tracks how users respond to nudges, what kind of language keeps them engaged, and when support is most helpful.
If a user tends to act on gentle, encouraging prompts, the platform leans into that tone. If another prefers direct guidance, Nutu adjusts. If someone consistently interacts in the evening, suggestions shift to meet that rhythm. This kind of responsiveness builds a relationship. Over time, the AI begins to sound less like a machine and more like a guide the user trusts.
Joe Kiani, Masimo founder, remarks, “Some of the early users that have been giving us feedback are saying really positive things about what it’s done for them.” That principle extends to communication. It is designed to adapt its tone and timing based on user behavior, making each prompt feel like a suggestion, not an instruction.
Supporting Emotional Clarity
Health is emotional. Fatigue, frustration, stress, and doubt can shape behavior. Effective AI guidance addresses more than tracking by blending mood check-ins and context into its language. If a person reports high stress or poor sleep, recommendations can shift toward recovery, such as a calm walk or an evening away from screens. If engagement rises, prompts can move toward more ambitious goals. This attention to emotional context keeps messages from feeling rigid and helps the guidance fit daily life.
Avoiding Medical Jargon
One of the most common pitfalls in digital health is language that feels overly clinical. Terms like circadian rhythm or metabolic flexibility may be technically accurate, but they often fail to resonate with the people meant to act on them. If the message sounds like a research paper, it creates distance rather than engagement.
AI tools can help close that gap by translating complex concepts into plain, everyday language. Instead of saying optimize sleep hygiene, a prompt might suggest trying unplugging from screens 30 minutes before bed. Rather than framing a recommendation around blood sugar dysregulation, the message could explain that your body may be reacting to when you eat. These adjustments don’t weaken the science. They make it accessible.
Clarity also builds trust. When users can immediately understand a suggestion, they are more likely to try it and notice its effects. Over time, this creates a feedback loop: people respond to guidance, the system refines its approach, and the language grows even closer to the individual’s own way of communicating. By replacing medical jargon with relatable cues, AI can turn complex health insights into practical steps that fit into daily life.
Timing Is Part of the Message
Timing shapes how people respond to guidance. A reminder that appears in the morning may feel motivating for one person and disruptive for another. A hydration suggestion during a busy commute might be ignored, while the same prompt later in the day could be welcome. AI can observe patterns such as when people engage quickly, dismiss a message, or return later, and then adjust delivery to fit individual routines. The result is support that feels better timed, more considerate, and easier to act on.
Feedback Loops Improve Clarity
AI coaching improves as it learns from the people using it. When a prompt feels off, users can give feedback, and when a suggestion resonates, that response is noted as well. Over time, these small signals refine the system so that its guidance reflects genuine preferences rather than assumptions.
Nutu is designed with this loop in mind, building a communication style that becomes more relevant as it adapts. Instead of guessing which tone or timing might work, it develops a profile grounded in real behavior. This responsiveness makes the guidance clearer, more personal, and easier to put into practice.
Clear Communication for Clinicians
AI-driven coaching can support clinicians by translating continuous streams of behavioral and health data into summaries that are easy to understand and act on. Traditionally, providers face dashboards crowded with numbers, graphs, and metrics that require time to interpret. In a busy clinic, that information often gets lost or ignored.
Well-designed AI tools shift the focus from raw data to usable insight. Instead of pages of charts, a clinician might see a concise overview highlighting sleep patterns, stress indicators, or activity rhythms that align with the patient’s own reports. The summaries are framed around practical actions, which means providers can move more quickly into meaningful discussion. Rather than decoding technical language, they can focus on what matters: how daily routines are affecting well-being and what adjustments could help.
This clarity also benefits the patient. When feedback is presented in plain language, it is easier to understand and apply. The conversation between provider and patient becomes more collaborative because both are working from the same clear set of observations. By turning data into dialogue, AI reduces the distance between clinical insight and everyday behavior, creating a smoother path from information to action.
Creating a System That Feels Human
When AI coaching feels thoughtful and personal, users stay engaged. When it sounds clinical or cold, they tune out. The most effective digital health tools recognize this fundamental truth. Their success isn’t measured by how advanced they sound, but by how human they feel, reflecting the language, pacing, and support people need to keep going. Ultimately, when users feel a tool truly understands them, they are more likely to listen and take action, successfully bridging the gap between clinical insight and real-world behavior.
