Healthcare Data Analyst — CMS IDR + FAIR Health (Plastic Surgery, Publication-Focused)

We are analyzing CMS No Surprises Act IDR datasets focused on plastic and reconstructive surgery, with the goal of producing both a peer-reviewed manuscript (Cureus) and a specialty benchmarking framework.

A data engineer is already building the dataset. This role is focused on analysis, interpretation, and framing, not raw data extraction.

You will work with:

• CMS IDR public use data (cleaned and structured)

• CPT-filtered plastic surgery subset

• FAIR Health benchmark data (provided)

Objective:

Identify and characterize reimbursement patterns in high-complexity, high-variance (“outlier”) cases, including:

• award vs QPA divergence

• provider vs payer dynamics

• alignment with independent benchmarks

Scope:

• Define defensible outlier thresholds and CPT groupings

• Analyze distribution and tail behavior (award/QPA, win rates, variability)

• Compare IDR outcomes to FAIR Health and other benchmarks

• Translate findings into clear, defensible conclusions suitable for publication and real-world reporting

This is not a dashboard project. We are looking for someone who can think critically about healthcare data, distinguish correlation vs defensible inference, and help structure findings in a way that is both publishable and practically useful.

Ideal background:

• Healthcare claims / reimbursement analysis

• CMS or large healthcare datasets

• Health economics or similar analytical experience

To apply, please include:

1. Relevant experience with healthcare data

2. Example of similar analytical work

3. How you would define “outliers” in this context

4. Your approach to comparing IDR outcomes to benchmark datasets

Looking to move quickly.

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