Healthcare Analytics & Data Science
I help healthcare organizations make sense of complex data, combining an actuarial foundation with data science and account management skills to turn messy claims data into decisions people can understand and act on. Below are a few tools I've built to sharpen that instinct.
Projects
Each of these stands on its own, but it's important to see the bigger picture. Each project below builds up to solve one larger overarching problem. With all the complexity in healthcare, how can an individual make an informed decision around their coverage?
All of the pieces below are designed to solve the question of which ACA plan is right for a member looking to buy healthcare in the DFW area (There were 171 plans in 2026). The ultimate goal is to provide a tool for members to see the best plan in terms of total member cost (premiums and OOP) based on some basic information.
First, we parse the PDF to extract the plan designs. Then we run some modeling in Excel. The Excel to DB tool gathers the data and uploads the changes to a Postgres database with full version history and allows sensitivity analysis, where you can recalculate values when changing one or more input. A Power BI report then sits on top of this data, allowing a user to enter their information and visually see the plan that is best for them and their family.
Parses Summary of Benefits and Coverage (SBC) PDFs into structured plan data including deductibles, out-of-pocket maximums, copays, drug tiers and more, using deterministic table-parsing rules.
See a sample run →A pipeline that aggregates data from multiple Excel workbooks into a single database, with built-in sensitivity analysis baked into the model.
Repo coming soon →A sample Power BI dashboard demonstrating report design and data modeling for healthcare analytics use cases.
Repo coming soon →