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The work at Healthlytics I am proudest of was a feature in the analytics product. Here is the problem it solved, how I approached it and what changed once it shipped.
The problem
The product surfaced health data, but a part of that workflow was slow and easy to get wrong, so people did not trust what they saw.
- Who it affected: the users relying on the analytics
- Why it mattered: bad or slow numbers meant decisions got delayed
- Why it was hard: the data was sensitive and had to stay correct end to end
My approach
I built the feature and the code behind it. The shape of it:
- Design: kept the data flow simple so it was easy to reason about and check.
- Stack: a web front end with a data layer behind it.
- Tradeoffs: favoured correctness over cleverness, since the numbers had to hold up.
What I shipped
| Piece | What it does | Status |
|---|---|---|
| the feature UI | surfaces the analytics to users | shipped |
| the data path | moves data between the app and its store | shipped |
| checks | catches bad data before it is shown | iterated |
Impact
Once it landed, people trusted the output more and spent less time second-guessing it. The workflow that used to stall stopped being a blocker.
What I learned
In health data, correctness is not a nice-to-have, it is the whole point. The lesson I took away was to design for that first and worry about polish later.