St. David's Medical Center · Healthcare · Austin, TX · 12 weeks
Patient data quality score raised from 58 to 91 in 12 weeks
St. David’s spent $3.2M on a population health platform that couldn’t launch because patient data didn’t meet quality standards. We dug into their demographic and encounter data, found the root causes of the issues, and fixed them within 12 weeks. Our work raised their data quality score from 58 to 91, allowing the platform to go live as planned.
Challenge
The population health platform needed a data quality score of at least 80 across 10 categories to activate. St. David’s initial score was 58. Their internal data team spent six months trying to fix the issues but made no real progress. The platform was paid for but wasn’t delivering any value.
Approach
In week one, we drilled into each failing dimension to find the root cause. Six out of ten problems came from one upstream ETL job that had been dropping records unnoticed for 18 months. We corrected the ETL process, restored the missing data from source systems, and created automated dashboards to monitor quality on the other four dimensions.
Outcome
By week 12, we raised the quality score to 91, surpassing the vendor’s 80-point benchmark. Our ETL fix recovered 340,000 patient records that had been missed before. The population health platform launched three weeks after we finished. We set up continuous quality monitoring that flags new data problems within 24 hours.
We ran into a hiccup when an ETL glitch scrambled six dimension tables.
Here’s what happened: one of our ETL jobs was silently dropping records whenever the zip code was missing. This flew under the radar for a good 18 months and caused the bulk of our data quality headaches. When we finally caught it, we fixed the job, and boom, most of the problems just vanished.
How We Found 340,000 Missing Records by Hunting Down Data Bugs
Alright, here’s the deal. The EHR system was missing a whopping 340,000 records. Huge gap, right? So, we built this backfill pipeline to fix it. We ran it overnight for four nights in a row, pulling in all the missing demographic and encounter data. And the cool part? We did it without disrupting anyone’s daily work. Smooth and clean.
How we set up nonstop quality checks on autopilot
We built a Power BI dashboard to track 10 key data quality metrics every day. If any of those fall below the threshold, the system sends a notification to the data team within 24 hours. This simple setup caught data quality problems early, no more surprises slipping through the cracks like before.
Results
- 58 to 91 Data quality score
- $3.2M Platform investment unblocked
- 340K Patient records recovered
- 12 wks From assessment to go-live
We invested $3.2 million in a platform that ended up not fitting our needs. Thinklytics found the issue in the first week, had it resolved by week four, and by week fifteen, we were fully operational. Now, it’s our go-to tool for managing population health.