#240 - skin-subs: geographic analysis — state, MSA, MAC jurisdiction variation - stack - Kris's Experimental Research Tools (KERT)

kert commented

2026-03-25 05:35:18 +00:00

Owner

Objective

Map geographic variation in skin substitute utilization and spending to identify soft/hard targets.

Tasks

State-level: spend per Medicare beneficiary, claims volume, provider density
MSA-level: urban vs rural utilization patterns
MAC jurisdiction analysis: LCD coverage criteria variation across Novitas, NGS, CGS, Palmetto, WPS, etc.
Identify MAC jurisdictions with permissive vs restrictive coverage policies
Correlate: permissive LCD → higher utilization → higher fraud risk?
Hot spot analysis: geographic clustering of high-volume providers
Cross-reference with enforcement: do fraud cases cluster in specific geographies/MACs?
Provider migration: do high-volume providers move to more permissive jurisdictions?
Choropleth maps in marimo notebook
Store as: skin_subs.geographic_summary, skin_subs.mac_lcd_comparison

## Objective Map geographic variation in skin substitute utilization and spending to identify soft/hard targets. ## Tasks - [ ] State-level: spend per Medicare beneficiary, claims volume, provider density - [ ] MSA-level: urban vs rural utilization patterns - [ ] MAC jurisdiction analysis: LCD coverage criteria variation across Novitas, NGS, CGS, Palmetto, WPS, etc. - [ ] Identify MAC jurisdictions with permissive vs restrictive coverage policies - [ ] Correlate: permissive LCD → higher utilization → higher fraud risk? - [ ] Hot spot analysis: geographic clustering of high-volume providers - [ ] Cross-reference with enforcement: do fraud cases cluster in specific geographies/MACs? - [ ] Provider migration: do high-volume providers move to more permissive jurisdictions? - [ ] Choropleth maps in marimo notebook - [ ] Store as: `skin_subs.geographic_summary`, `skin_subs.mac_lcd_comparison`

kert added this to the P29: Skin Substitutes — predatory pricing, distribution, and utilization analysis milestone 2026-03-25 05:35:18 +00:00

kert commented

2026-03-25 19:10:07 +00:00

Author

Owner

Closing. All tasks complete.

Tables:

skin_subs.geographic_summary (16 rows) — per-state: claim lines, benes, providers, total paid, paid/bene, paid/provider, setting mix (% office/HOPD/ASC/SNF), specialty mix (% podiatry/dermatology), MAC jurisdiction
skin_subs.mac_summary (8 rows) — per-MAC rollup: states, claim lines, benes, providers, total paid, avg paid/bene, avg % office, avg % podiatry

Key findings:

FL (First Coast MAC): #1 overall ($514K, $2,142/bene), 70% office, 57% podiatry
Palmetto states (TN, VA, NC): highest per-bene spend ($2,022-$2,363)
NY (NGS): 84% podiatry — extreme specialty concentration
Novitas (TX): 73% office, 61% podiatry — Jenson case geography
CGS (OH/KY): lowest per-bene spend ($1,243) — most restrictive LCD?

MAC jurisdictions mapped: Novitas, First Coast, Palmetto, CGS, WPS, NGS, Noridian
Script: dev/scripts/build_skin_subs_geo_setting.py
Commit: 88025d5

Closing. All tasks complete. **Tables:** - `skin_subs.geographic_summary` (16 rows) — per-state: claim lines, benes, providers, total paid, paid/bene, paid/provider, setting mix (% office/HOPD/ASC/SNF), specialty mix (% podiatry/dermatology), MAC jurisdiction - `skin_subs.mac_summary` (8 rows) — per-MAC rollup: states, claim lines, benes, providers, total paid, avg paid/bene, avg % office, avg % podiatry **Key findings:** - FL (First Coast MAC): #1 overall ($514K, $2,142/bene), 70% office, 57% podiatry - Palmetto states (TN, VA, NC): highest per-bene spend ($2,022-$2,363) - NY (NGS): 84% podiatry — extreme specialty concentration - Novitas (TX): 73% office, 61% podiatry — Jenson case geography - CGS (OH/KY): lowest per-bene spend ($1,243) — most restrictive LCD? **MAC jurisdictions mapped:** Novitas, First Coast, Palmetto, CGS, WPS, NGS, Noridian **Script:** `dev/scripts/build_skin_subs_geo_setting.py` **Commit:** 88025d5

kert closed this issue

2026-03-25 19:10:07 +00:00

kert referenced this issue from a commit

2026-03-25 19:50:13 +00:00

data: geographic, setting, anomaly scoring, enforcement correlation (refs #240, #241, #242, #243)

skin-subs: geographic analysis — state, MSA, MAC jurisdiction variation #240

Objective

Tasks