Reduce cognitive load with explanatory data
Trends provide health insights by detecting individuals’ routine changes through data science and our AI model. It helps customer to identify potential diagnoses sooner for their loved ones.
Key contributions — Leading design direction + strategy + execution, ux, team collaboration, research & workshops
Main Objectives
1. Reduce at least 20% of customers everyday workload on everyday tasks
2. Reduce unnecessary human errors on data research and analysis
3. Improve product data structure and speedup the loading time by at least 50%
4. Increase the volume of business acquired
Outcomes
~73%
Significant speed up on users everyday tasks
~40%
Reduction on return tickets from users systems (Error or additional inquiry)
~60%
Significant Loading speed improvement
3
New businesses onboarded
What We Are Trying To Solve
User spent significant time reviewing individuals’ health data. Managing the workload and human error became challenging when more individuals onboard.
High Cognitive Load
Users felt overwhelmed by the amount and complexity of data they had to analyse every day.
Product Performance
Users were frustrated when comparing health data because of the platform’s slow loading time.
Human Error
The level of complexity of daily tasks lead users feared of making mistakes.
Principles & Strategy That Drive Business Impact
Work Smarter Not Harder
The solution should give users focus and clarity. Delivering the health insight in a straightforward solution.
Keep It Simple
Unify data presentation and reduce the need for extra processing by users.
Speed It Up
Improve our data structure to provide a faster and smoother user experience for their daily tasks
Insights & learning drive our execution
With the learning on the existing health insights, which presented users with a overwhelming amount of data without clear structure. We defined our improvements.
❌ Inconsistent ❌ Indirect ❌ Improvement Needed
1. Simplify & Unify
16 Data attributes
11 → 3 Unified modules
We simplify the health data presentation from 11 down to 3 unified modules to summarise individuals’ health insights.
Counting — Countable data that continuously accumulates from zero.
Range/ Rate — The measurements that fall between two specific values (ranges).
Time — Displaying time-based data (specific times or periods during the day).
2. Focus & Clarity
Exploratory Data vs…
The previous system display data across different charts, where users need to explore and identify unusual health pattern. This approach is time-consuming and increases users workload.
Explanatory Data
Instead of make them search for Wally, we bring them Wally.
We simplify the logic and deliver the pattern change to users directly as Trends. It create focus so users can manage their workload by ignoring unnecessary and distracting data.
3. Reduce Friction
60% Performance Speed improved
With Data Science and Back End Team amazing work, we managed to achieve a significant faster loading speed with the new Trends approach.
🙅♂️-Functional Collaboration
Cross-functional Workshops
Prioritising tasks for different teams based on difficulty and potential technical limitations
Design Execution
Define core function specification based on the design direction
Proof of Concept
Evaluating our decision with fully functional product performance
Qualitative Testing
Measuring the impact against our defined objectives
Challenges — Data analysis is complex, unexpected scenarios happens, data collection could went wrong…
Delivery & Impact
MySense Trends Reduced friction in data delivery by focusing on the most significance health pattern changes. This drives a lighter workload and faster performance.
User Experience
✅ Simplify & Unify
Using 3 consistent modules to deliver 16 data attributes. Highlighting specific changes in health data.
✅ Focus & Clarity
Applying Trends to users’ daily tasks can simplify their workload by reducing unnecessary and distracting data.
✅ Reduce Friction
Significant faster performance provide better and lighter experience on data delivery.
A/B Testing
We test the Trends along side the original function. With qualitative interview and feedback sections, we measure the impact on both users and business.
~73%
Significant speed up on users everyday tasks based on 30 individual tasks.
”The new approach helped me prioritise tasks effectively. I allocate more time to those who genuinely require attention in stead of mapping data on everyone.”
~38%
Reduction on return tickets from users systems (Error or additional inquiry)
“With the new approach, I can work on the require actions based on the Trends insight and take note right away in stead of copy and paste into a work document. It would definitely reduce human errors.”
60%
Significant Loading speed improvement.
“It is so much faster!”
Learnings
We’ve identified a few areas that we need to address in the next version, as per users’ feedback.
There are Improvements to enhance the functionality while also introducing some specific features that we could incorporate into the product.
*All user data and quantities in this case study have been adjusted in accordance with individuals’ data protection policies and the company's NDA.
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