Applied projects
Beyond academic research, I apply health economics and data science methods to real-world healthcare operations problems; from cost trend analysis to evaluating the financial impact of clinical programs. Below are selected applied projects demonstrating this work using SAS, SQL, Python, and data visualization tools.
P#1: Cost Driver Analysis
Objective: Identify the primary drivers of healthcare cost trends across a member population, distinguishing utilization patterns from unit-cost inflation.
Data & Methods:
Built a synthetic claims dataset (member demographics, diagnosis codes, procedure codes, billed/paid amounts, ...) modeled on CMS public use files.
Used SQL to aggregate claims by service category, provider type, and member cohort.
Applied regression decomposition in SAS, Python (pandas, statsmodels) to separate the effect of utilization frequency vs. price per service on total spend.
Key Findings:
Small percentage of high-cost claimants accounted for a disproportionate share of total spend — consistent with typical 80/20 healthcare cost distributions.
Quantified how much of year-over-year cost growth was driven by unit cost vs. increased utilization.
P#2: Evaluating the Financial Impact of a Clinical Program
Objective: Estimate whether a hypothetical care management program (e.g., diabetes management outreach) produced a net cost savings for enrolled members.
Data & Methods:
Designed a pre/post, treatment-vs-comparison-group evaluation framework — the standard approach for measuring ROI of clinical interventions.
Used propensity score matching in Python to control for demographic and risk differences between enrolled and non-enrolled members.
Modeled projected cost avoidance vs. program operating cost to calculate net financial impact and payback period.
Key Findings:
Built a reusable evaluation template that could be applied to any new clinical program or vendor solution to answer: "did this pay for itself?"
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