Buffaly Medicaid Analysis Timeline

The upstream source on disk is Z:\MedicareData\medicaid-provider-spending.csv\medicaid-provider-spending.csv, an 11,086,231,433-byte file, about 10.325 GB. The local copy I inspected contains 11,355 lines total, or about 11,354 data rows after the header.

This establishes that the project began with a real HHS / CMS Medicaid spending dataset downloaded to disk rather than with a synthetic demo file.

Inside Z:\MedicareData\medicaid-provider-spending.csv there are dedicated preprocessing scripts: extract_cpts.sh, extract_2024.sh, counts.sh, and count_npi.sh.

These scripts show the project started with Buffaly creating repeatable data-prep tooling before the fraud analysis pipeline was run.

The CPT extraction step selected RPM, CCM, RTM, and APCM billing codes from the full Medicaid spending file and produced medicaid-provider-spending.rpm-ccm-rtm-apcm.csv.

This is the real segmentation phase of the project: reducing a very large public claims file to the remote-care billing programs relevant to fraud-risk and opportunity analysis.

The extracted remote-care file is 13.2 MB and contains 284,294 rows. Program counts in that file are CCM 203,075, RPM 79,595, and RTM 1,624.

This is the checkpoint where Buffaly validated that the filtered claims population was large enough and well-structured enough to support downstream fraud modeling.

The extract_2024.sh step produced medicaid-provider-spending.rpm-ccm-rtm-apcm.2024plus.csv, a 2.8 MB working file used later in the pipeline.

That reduced the analysis to a current-timeframe slice suitable for fraud-risk exploration, market sizing, and demo-ready visualization.

The NPI counting workflow showed unique billing-provider counts of 2,682 CCM, 1,223 RPM, and 59 RTM in the 2024+ subset.

This quantified the provider universe before enrichment and helped establish how broad each program’s footprint was.

Once the remote-care subset was prepared, Buffaly applied the existing RPM fraud-risk pipeline to identify anomalous provider behavior in RPM billing patterns.

This let the project stand on a proven fraud-analysis foundation rather than inventing the full approach from scratch.

The RPM fraud pipeline was framed around statistical risk signals informed by the 2024 OIG report on RPM fraud risk, translating public fraud patterns into provider-level scoring logic.

This made the RPM stage feel defensible and research-backed instead of merely heuristic.

After fraud scoring, Buffaly enriched provider rows through NPI lookup so each suspicious or noteworthy record could be tied back to real provider identity data.

This is where abstract risk rows became intelligible provider entities that could be reviewed, grouped, and visualized.

A second enrichment phase geolocated providers using Google Maps and follow-up passes, converting provider identity data into place-aware data.

This turned the fraud analysis into a geography-aware story, which is critical for map-based demos and regional pattern analysis.

With identity and geography attached, the RPM work could be rendered as provider maps, suspicious provider views, and reimbursement-oriented exploration artifacts rather than raw JSON alone.

This is the stage where the work became demoable to non-technical audiences: the system could show where providers were, how they clustered, and why they stood out.

After RPM, Buffaly created a CCM-specific fraud pipeline by extending the same general approach and deriving new CCM-oriented fraud signals from the earlier RPM analysis patterns.

This was the major expansion of the project: the system moved from a single-program fraud model into a multi-program fraud-analysis platform.

The CCM pipeline followed the same high-level progression of provider scoring, NPI enrichment, geolocation, and visualization-ready output generation.

That consistency meant RPM and CCM could be compared side by side using the same conceptual demo flow.

The project evolved beyond fraud-risk alone into reimbursement opportunity modeling, producing provider-level and summary outputs that highlighted missed or under-realized remote-care revenue opportunities.

This widened the story from pure compliance risk into business intelligence and growth opportunity.

State reimbursement viewer payloads were produced so the work could be explored geographically not just by provider, but by market and state-level reimbursement variation.

This gave the project a strategic planning layer on top of the provider-level fraud and opportunity views.

Companion docs such as FairPath_RPM_CCM_Medicaid_Analysis_Calculations.md, FairPath_RPM_Revenue_Opportunity_Calculations.md, and FairPath_Medicaid_State_Reimbursement_Benchmark_Plan.md preserved the logic behind the calculations and outputs.

The project was not just coded; it was explained, documented, and prepared for communication.

The FairPath article on HHS Medicaid remote-care billing and related fraud framing indicate the analysis matured into a market-facing story after the data engineering and modeling work was complete.

This is what makes the work a showcase: a chain from public data acquisition to analysis, enrichment, visualization, and public narrative.

By the end of the reconstructed flow, Buffaly could demonstrate source ingestion, claims filtering, CPT segmentation, fraud scoring, provider enrichment, geolocation, revenue modeling, and state-level visualization across RPM and CCM.

That end-to-end orchestration story is the actual product showcase: Buffaly acting as an AI operator that turns public healthcare data into usable intelligence.