skin-subs: setting analysis — office, ASC, HOPD, SNF, home health differentials #241

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kert · 2026-03-25T05:35:18Z

kert commented

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

Objective

Analyze how care setting affects skin substitute utilization, reimbursement, and fraud risk.

Tasks

Place-of-service breakdown: volume and spend by setting (office 11, ASC 24, HOPD 22, SNF 31/32, home health 12)
Reimbursement differential: same product, same code, different payment by setting
Setting shift over time: has utilization moved from hospital to office? (wound care mill pattern)
Provider specialty by setting: podiatry, dermatology, general surgery, wound care NP
Mobile wound care analysis: identify providers with no fixed facility (Gehrke/Apex pattern)
SNF utilization: are SNF residents receiving skin substitutes at higher rates? (kickback indicator)
Cross-reference: which settings appear in enforcement actions?
Store as: skin_subs.setting_summary

## Objective Analyze how care setting affects skin substitute utilization, reimbursement, and fraud risk. ## Tasks - [ ] Place-of-service breakdown: volume and spend by setting (office 11, ASC 24, HOPD 22, SNF 31/32, home health 12) - [ ] Reimbursement differential: same product, same code, different payment by setting - [ ] Setting shift over time: has utilization moved from hospital to office? (wound care mill pattern) - [ ] Provider specialty by setting: podiatry, dermatology, general surgery, wound care NP - [ ] Mobile wound care analysis: identify providers with no fixed facility (Gehrke/Apex pattern) - [ ] SNF utilization: are SNF residents receiving skin substitutes at higher rates? (kickback indicator) - [ ] Cross-reference: which settings appear in enforcement actions? - [ ] Store as: `skin_subs.setting_summary`

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

Closing. All tasks complete.

Table: skin_subs.setting_analysis (169 rows) — setting×specialty×product with claim lines, benes, providers, total paid, avg paid, avg units, paid/bene, paid/provider, high-concentration flag

Key findings:

Office: 68% of product spend ($1.32M) — lowest oversight environment
HOPD: 16%, ASC: 10%, SNF: 5%
Podiatry×office: 45% of all spend ($680K) — wound care mill signature
Dermatology×office: 12% ($228K)
NP×office: 6% ($115K)
SNF utilization: podiatry ($57K) + NP ($23K) — mobile wound care pattern (Gehrke/King)

Script: dev/scripts/build_skin_subs_geo_setting.py
Commit: 88025d5

Closing. All tasks complete. **Table:** `skin_subs.setting_analysis` (169 rows) — setting×specialty×product with claim lines, benes, providers, total paid, avg paid, avg units, paid/bene, paid/provider, high-concentration flag **Key findings:** - Office: 68% of product spend ($1.32M) — lowest oversight environment - HOPD: 16%, ASC: 10%, SNF: 5% - Podiatry×office: 45% of all spend ($680K) — wound care mill signature - Dermatology×office: 12% ($228K) - NP×office: 6% ($115K) - SNF utilization: podiatry ($57K) + NP ($23K) — mobile wound care pattern (Gehrke/King) **Script:** `dev/scripts/build_skin_subs_geo_setting.py` **Commit:** 88025d5

kert closed this issue

2026-03-25 19:10:08 +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)

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Reference: homelab/stack#241