<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Data on Rusty Bower</title><link>https://www.rustybower.com/categories/data/</link><description>Recent content in Data on Rusty Bower</description><generator>Hugo -- gohugo.io</generator><language>en</language><copyright>© Rusty Bower</copyright><lastBuildDate>Sat, 14 Feb 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://www.rustybower.com/categories/data/index.xml" rel="self" type="application/rss+xml"/><item><title>Finding Fraud in $1 Trillion of Medicaid Data with DuckDB</title><link>https://www.rustybower.com/posts/medicaid-fraud-detection-duckdb-227m-rows/</link><pubDate>Sat, 14 Feb 2026 00:00:00 +0000</pubDate><guid>https://www.rustybower.com/posts/medicaid-fraud-detection-duckdb-227m-rows/</guid><description>&lt;p&gt;CMS recently published provider-level Medicaid spending data from T-MSIS — every fee-for-service, managed care, and CHIP claim from 2018 through 2024, aggregated by billing provider, procedure code, and month. 227 million rows. $1.09 trillion in payments. I wanted to see what falls out when you run some basic fraud heuristics against it.&lt;/p&gt;</description></item></channel></rss>