Foresee is a time based understanding system that helps users make sense of recurring symptoms and decide when to take action.
The Problem
Positioning
Most health tools track data or manage serious sickness but they do not help people decide if small symptoms matter. Foresee fills this gap by helping users turn scattered signals into actionable understanding over time.
Research
Online Questionnaire
To validate whether this “gray zone” is a real and widespread problem, I conducted a survey with 46 participants.
User Interview
Competitive Analysis
I analyzed the market to understand how existing tools fail to support users in interpreting symptoms over time.
Data tracking apps
Symptom logging apps
Require frequent manual input but give fragmented, delayed insights, making it hard to see value in continuous logging.
Symptom checker apps
Offer one time answers but do not support ongoing symptoms, making it hard to see patterns over time.
As a result, users must invest effort to interpret fragmented data themselves, often without clear results, which reduces motivation to continue tracking.
Key Insights from Research
How might we help people understand their symptoms over time so they can decide when to take action?
Design Principles
Design Solution Flow
Foresee helps users understand patterns that emerge over time, guiding them from sensing symptoms to making informed decisions.
Integrated Data Capture
Path A: Quick Check In
To address the high effort of continuous logging, the system introduces quick check ins triggered only by significant body signal changes, so that users can log symptoms with minimal effort.
Path B: AI Chat Logging
To address uncertainty when symptoms feel complex, the system allows users to log through AI chat, so that they can capture detailed context and receive more meaningful input.
Pattern Discovery
To address the difficulty in interpreting recurring symptoms, the system reveal patterns that emerge over time, so that users can understand when and why symptoms occur.
AI Generated Health Summary
To address the gap between tracking and action, the system generates structured health summaries, so that users can better communicate their condition and decide next steps.
Process
I mapped Personas to identify Drop off Risks and built Design Interventions to solve them. This System Diagram shows how passive data generates health insights.
Usability Testing
8 participants with recurring symptoms tested onboarding, check ins, AI chat, insights, and export flows.
Iteration
I tested every flow to find friction in user understanding. These updates focus on improving the setup process and data readability.
1. Restructuring onboarding to reduce cognitive load and build trust
Early testing showed onboarding felt confusing and asked for too much too soon. People didn’t really get the features before trying it. So I let them see the value first, then moved account setup and data connection later.
2. Improving pattern clarity with step by step details
Testing showed users thought the 30 day chart was just one day of data and didn’t understand how patterns were found. The original layout did not have clear hierarchy or enough context.
Design Elements
The visual design balances clarity and comfort. A clear structure builds trust and supports accurate data, while lighter colors reduce visual heaviness. This balance supports emotional comfort without reducing clarity.
Reflection
Designing Foresee shifted my focus from building tracking screens to helping users interpret health signals. In early health stages, people are not looking for diagnoses. They want to know if a change in their body is worth paying attention to. Because of this, the design helps users see patterns across symptoms, lifestyle, and time so they can better judge when it may be worth talking to a doctor.