Helping new CPAP users stick with therapy


Software Engineer
During my internship at Philips Sleep & Respiratory Care, I worked on improving the early CPAP experience for DreamStation—Philips’ core CPAP device line.
I helped define an early-use experience that makes CPAP feel more comfortable, and less overwhelming for first-time users. To do this, I launched a workshop to align teams on adaptive UI directions, then translated concepts into prototypes.
This work resulted in a validated set of design principles and prototype concepts that shaped a more supportive CPAP experience and established a clear direction for future product development.
Complex setup: First-time use involves unfamiliar medical terms and multiple device settings, often without clinical guidance at home.
Unclear feedback: Users lack clear signals to know whether the device is working correctly.
Rising competition: Products like ResMed are setting higher expectations for intelligent, user-friendly experiences.
Medical safety constraints: Any intelligent assistance must be predictable, transparent, and non-intrusive.

I began with immersive research to understand how users experience complexity during therapy, what breaks trust or confidence along the way, and what guidance they need to feel supported.
So I took these steps:
After identifying the structural tension between medical complexity and cognitive burden, the next challenge was translating this insight into concrete, low-risk design directions. Rather than jumping directly into solutions, I facilitated a cross-functional workshop to explore how Adaptive User Interface (AUI) principles could be applied in a high-stakes medical context—while respecting clinical safety, predictability, and user control.
The workshop served as a decision-making tool: to align stakeholders, evaluate trade-offs, and narrow a broad design space into a small set of viable AUI patterns.
I opened the workshop by framing Adaptive UI specifically for high-stakes medical devices—focusing on gradual adaptation, predictable behavior, and preserving user control.
(Full slides available as a PDF)

Participants shared examples of Adaptive UI they trusted—and patterns that felt intrusive, confusing, or unsafe.
This step helped surface shared intuitions around predictability and transparency.

Inspired by the Drunk Island framework, we mapped ideas across impact and implementation difficulty, then converged on patterns that balanced cognitive relief with feasibility and safety.


After the workshop, I synthesized the discussion into a set of shared design principles and interaction patterns, which became the foundation for subsequent prototyping.



The workshop outputs clarified which AUI patterns were both desirable and viable—directly informing the strategy that followed.
Based on the workshop outcomes, I framed Adaptive UI not as automation, but as a low-risk interaction layer that manages complexity over time.
This strategy followed three principles:
Grounded in the strategy and workshop insights, I explored four interface concepts for the DreamStation embedded interface—each operationalizing AUI principles at varying levels of guidance, visibility, and user control.
This set of concepts enabled us to compare how Adaptive UI could reduce early cognitive burden while maintaining safety-critical constraints.
This project taught me how to design AI in medical contexts—not by adding intelligence, but by carefully shaping when and how it appears.
By working closely with designers, engineers, and clinical partners, we aligned on a low-risk, adaptive approach to supporting early CPAP use.
I’m thankful for the mentorship and cross-functional support that helped me grow as a designer in a medical context.