Travis County Health Department · Government · Austin, TX · 12 weeks
Public health surveillance dashboards deployed to 180 staff
Travis County Health Department relied on 14 manual Excel reports that required three full-time analysts. We replaced all of them with automated Power BI dashboards, cutting $620,000 in annual labor costs and delivering real-time disease tracking.
Challenge
The epidemiology team spent four of every five working days manually compiling 14 weekly and monthly Excel reports. They extracted data from four different systems, formatted it by hand, and distributed the reports themselves. This left no time for meaningful analysis, and weekly updates meant real-time disease tracking was out of the question.
Approach
We extracted data from all four source systems and consolidated it into a single Power BI dataset, updating it daily. We recreated 14 reports as interactive Power BI dashboards with drill-down features and set up automated distribution. Additionally, we developed a disease surveillance dashboard that refreshed every four hours during outbreaks.
Outcome
By week 12, we replaced 14 static reports with interactive Power BI dashboards. This cut annual reporting labor costs from $620K to $80K. The epidemiology team regained four of every five working days, shifting focus from data prep to analysis. Three weeks after launch, during a norovirus outbreak, the new real-time dashboard helped the team identify the source four days faster than before.
We churned out 14 reports in 12 weeks flat and cut the report turnaround time in half.
Here’s the deal: we had fourteen reports, and eleven of them were all grabbing data from the same four sources. Instead of dealing with each one separately, we just merged those into a single dataset. That saved us a ton of time on data prep and let us focus on building the visuals. The other three reports? They each needed their own unique data setup.
How We Built Real-Time Alerts to Spot Problems the Second They Pop Up
Weekly reports were too slow for what we needed. So, we built a dashboard that updates every 4 hours. That way, the team could watch the cases almost in real time. When a norovirus outbreak popped up three weeks later, they found the source four days faster.
We showed 180 folks how to use self-service tools. That way, they could grab their own data without bugging the analysts every time.
We got together for six sessions with 180 people from the health department. Our goal? Teach them how to build automated reports and get comfy with Power BI’s self-service tools. Fast forward a month, and 94 of every 100 said they were happy with how everything worked out.
Results
- $620K Annual reporting labor saved
- 14 Manual reports automated
- 180 Staff with real-time access
- 4 hrs Surveillance data refresh cycle
Before, my epidemiologists were spending around 80% of their time just putting together Excel reports. Now, they use that time to focus on the actual epidemiology work. The real-time surveillance dashboard has already changed how we manage outbreaks.