Healthcare Quality Dashboards
Predictive quality intelligence, not just another health app
Compliance data is usually a report nobody reads. This turns it into dashboards that show patterns: P-charts, Pareto analysis, heat maps, and compliance calculations that point to where harm is likely before it happens.
Tools I used
How it started
Quality meetings were full of numbers nobody could act on. I wanted charts that end in a decision.
What actually happened
P-charts told us whether a change was real variation or just noise, and Pareto analysis told us which few causes carried most of the harm. The stack was whatever was already available — Excel, Access, Microsoft Lists, a DynamoDB store, a Netlify front end. The value was never the tooling. It was refusing to report a number without also reporting what to do about it. Once the dashboards had an owner and an action per chart, the meetings got shorter and the improvements got real.
Moments that changed the build
- Me: "Is this change real or noise?" → Control charts answered it honestly, sometimes uncomfortably.
- Me: "Which twenty percent causes most of it?" → Pareto turned a long list into three priorities.
- Me: "Every chart needs an owner." → Dashboards stopped being decoration.
What broke
The first dashboard was beautiful and useless — lots of charts, no decisions. I deleted half of it.
What I learned
A metric without an owner and an action is decoration. Predict, then act, then measure again.
The ebook from this project
- Read it
The Healthcare Quality Dashboards Story
Predictive quality intelligence, not just another health app
Ebook · $9.00