Discover potential diagnoses through user routines
The Trends feature in the MySense app leverages data science to provide health insights, helping customer identify potential diagnoses early by detecting individuals’ routine changes and preventing irreversible loss.
Key contributions — Leading and executing functional designs, team collaboration, research & workshops
Main Objectives
Reduce at least 20% of customers everyday workload on everyday tasks
Reduce unnecessary human errors on data research and analysis
Improve product data structure and speedup the loading time by 50%
Increase the volume of business acquired in Q4 2024 and Q1 2025
Outcomes
~73%
Tasks speed improved, significant speed up on customers everyday tasks
~38%
Reduce of return tickets from customers systems (Error or additional inquiry)
~60%
Significant Loading speed improved
3
New businesses onboarded between two quarters
What We Are Trying To Solve
Although we previously had the technology to present data to clinicians, research showed that they often spent significant time exploring individuals’ health data without clear guidance. As more individuals joined the service, clinicians faced increasing challenges managing their workload and grew concerned about human error of critical health issues.
Our goal is to define and execute a feature that aligns with their workflow, alleviates their frustration, and establishes clear metrics to measure the impact and effectiveness of the new design approach.
Principles & Strategy That Drive Business Impact
Work Smarter Not Harder
By providing more efficient use of the product, we can reduce clinicians workload for each individual. This becomes increasingly important as more individuals join the platform.
Keep It Simple
Monitoring data from big volume of individuals is stressful. A better way of presenting data could help reduce errors that occur when managing large amounts of information.
Speed It Up
Instead of loading raw data directly into various charts, improving backend data processing can reduce loading times and bugs. This would significantly improve the product performance.
Insights & Learning Drive Our Decisions
Based on the learning from interviews, we simplify the health data into types. With the new data structure, we can summarise them into health insights to provide a direct delivery of individual’s health and routine changes.
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).
Execution & 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
The System
With the defined data types, we designed a methodology to apply the new data structure as cards. This approach aims to summarise recent significant health pattern changes by comparing the recent collected data to the previous 7 days data. Essentially, a card’s appearance indicates something unusual, which addresses the issue of indirect data delivery.
Level of Complexity
By collaborating with the clinician, we both believed that the explanatory data was achieving our intended purpose. However, we also wanted to preserve the ability of discovery from exploratory data in specific cases. Therefore, we decided to structure our data delivery in two levels, based on the level of complexity that the they wanted to see.
User Experiences
All cards in Trends are interactive, enabling clinicians to ‘zoom in’ on a specific topic they wish to deep dive into. This feature allows them to choose the level of complexity they prefer, such as reviewing daily data from the past 7 days or leaving notes. Additionally, it directs them to the relevant sections of the product.
Qualitative A/B Testing
We launched the first version of Trends following the POC. We implemented some changes based on identified limitations and speed testing of the process to ensure the functionality’s reliability.
Exploratory Data vs…
The previous system display data across different charts for clinicians to explore and identify unusual patterns in individuals' routines. This approach is time-consuming and increases clinician workload, especially since some clinicians need to monitor around 50 individuals per day.
Explanatory Data
Instead of make them search for Wally, we bring them Wally.
The new approach work well for the user cases and align with the new data structure. It allow us to simplify the data presentation so we can bring out the core message to the clinician directly.
~73% Faster
Tasks speed improved
It is socking to see a huge improvement in the completion time of tasks involving over 50 individual tasks. Clinicians have expressed that the new approach enables them to prioritise tasks effectively, as they no longer waste time on individuals who are in a routine of good health. This allows them to allocate more time to those who genuinely require attention. They mentioned it is helpful specially when more individuals on boarded.
~38% Less
Error or additional inquiry reduced
With the new approach of the data structure, we hide unrelated data, which helps clinicians focus on actual health issues from individual health changes, allowing them to better examine any unusual changes instead of sifting through all data to find them.
~60% Faster
Loading speed improved (landing page)
The simplification and reduction of the data is definitely help a lot with the loading speed. With Data Science and Back End Team amazing work, we managed to achieve 60% faster loading speed. Even complex-level data can now be processed at a faster speed with this new approach.
What’s Next
Although the performance of Trends exceeds our expectations based on our measurements, we’ve identified a few areas that we need to address in the next version, as per clinicians’ feedback.
We’ve analysed all the feedback and come up with the following design directions to enhance the functionality of Trends 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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