JIL's in-house Tier 1 detection ran on the live Medicare Inpatient Hospitals dataset (145,879 hospital x MS-DRG (Medicare Severity Diagnosis-Related Group) rows we ingested from data.cms.gov) plus the 27 other federal sources in our backbone. 25 hospitals surfaced with significant per-DRG payment outliers vs the national cohort. Ava, our in-house agentic AI, then groups every finding by archetype, separates legitimate case-mix variance from suspicious billing patterns, and routes the genuinely-anomalous candidates to the right Tier 2 evidence path. Throughout this page: DRG = Diagnosis-Related Group (the Medicare classification each inpatient stay is grouped under for payment), CCN = CMS Certification Number, CERT = Comprehensive Error Rate Testing, LCD/NCD = Local/National Coverage Determination, MAC = Medicare Administrative Contractor, POS = Place of Service / Provider of Services file, DSH = Disproportionate Share Hospital adjustment.
Every signal in this POC comes from CMS / HHS / Treasury public data we ingest live. No subscription. No customer engagement data. No PHI. Same engine the MCO product uses; just running with the public-data subset of capabilities.
Pre-load dedup + post-load cross-version dedup run on every incremental pull. Counts below are live row totals from our database, not estimates. Hit the gateway directly.
| Source | Cadence | Last refresh | Rows | Pre-load dedup |
|---|---|---|---|---|
| CMS Provider+Service (NPI x HCPCS, capped 500K) | Annual | 2026-04-28 | 500,006 | on (NPI+HCPCS+POS) |
| CMS DMEPOS by Referring Provider and Service | Annual | 2026-04-28 | 497,988 | on (NPI+HCPCS) |
| CMS Part D Prescriber by Drug | Annual | 2026-04-28 | 475,681 | on (NPI+NDC+drug) |
| CMS Geography+Service benchmarks | Annual | 2026-04-28 | 268,640 | on (geo+HCPCS+POS) |
| Medicare Inpatient Hospitals - by Provider and Service | Annual | 2026-04-28 | 145,881 | on (CCN+DRG) |
| Medicare Outpatient Hospitals - by Provider and APC | Annual | 2026-04-28 | 116,799 | on (CCN+APC+HCPCS) |
| Provider of Services (POS) file | Quarterly | 2026-04-28 | 44,429 | on (CCN) |
| OFAC SDN List + alt + add | Daily | 2026-04-28 | 18,899 | on (sdn_id) |
| Medicare SNF Post-Acute Care PUF | Annual | 2026-04-28 | 14,162 | on (CCN) |
| Medicare Home Health Post-Acute Care PUF | Annual | 2026-04-28 | 8,467 | on (CCN) |
| Medicare Hospice Post-Acute Care PUF | Annual | 2026-04-28 | 5,772 | on (CCN) |
| CERT FY2024 root-cause library | Annual | 2026-04-28 | 27 | on (detector_id) |
| MAC LCD Jurisdiction Map | Quarterly | 2026-04-28 | 12 | on (mac_id) |
| Medicare Coverage Database (NCD + LCD) | Quarterly | 2026-04-28 | 8 | on (rule_id) |
| NPPES NPI Registry (bulk monthly, ~1 GB ZIP) | Weekly diff | queued | ~7M | long-poll-pending |
| OIG LEIE / SAM exclusions / OIG enforcement | Daily-Monthly | queued | -- | anonymous-blocked |
| Open Payments / HCRIS / DOJ Strike Force / Preclusion | Annual-Quarterly | queued | -- | URL-pending |
Tier 1 has eight investigation models. Six run today on the live federal data backbone. Two need customer-side data and activate at Tier 2: bank fingerprinting (3) requires wire records under BAA; premise / volume detail (4, 6) reaches full strength once USPS / Street View AI / ATTOM credentials are wired. The findings table in Section 03 shows which of the 28 federal sources contributed signal to each row.
Each row below is a hospital that ranks as a per-Diagnosis-Related-Group payment outlier (z-score ≥ 3 vs the national cohort) on five or more distinct MS-DRGs (Medicare Severity Diagnosis-Related Groups) in calendar year 2022. Tier 1 ran a single pass over the live ingest. The "Signals" column shows which of the 28 federal sources contributed signal to that finding. Statistical outlier is not adjudicated finding. Ava's job (Section 04 below) is to separate legitimate case-mix from suspicious billing - in many cases the answer is "academic medical center, expected case-mix variance, no Tier 2 needed."
| # | Hospital | State | Diagnosis-Related-Group outliers (≥2σ / ≥3σ) | Overage $ | Discharges | Tier 1 signals fired | Federal sources cross-referenced |
|---|---|---|---|---|---|---|---|
| 1 | Stanford Health Care | CA | 140 / 78 | $196,647,442 | 10,081 | DRG-OUTVOLREF | InpatientGeo benchmarkPOSCERT |
| 2 | New York-Presbyterian Hospital | NY | 58 / 8 | $146,775,162 | 25,428 | DRG-OUTVOL | InpatientGeo benchmarkPOS |
| 3 | University Of Maryland Medical Center | MD | 80 / 73 | $142,304,911 | 3,537 | DRG-OUTDRG-MULTIREF | InpatientGeo benchmarkCERTMAC |
| 4 | Johns Hopkins Hospital | MD | 122 / 89 | $138,398,802 | 7,040 | DRG-OUTDRG-MULTIREF | InpatientGeo benchmarkCERTMAC |
| 5 | UCSF Medical Center | CA | 113 / 57 | $134,964,776 | 6,348 | DRG-OUTDRG-MULTIREF | InpatientGeo benchmarkPOS |
| 6 | Ronald Reagan UCLA Medical Center | CA | 57 / 37 | $74,930,976 | 3,015 | DRG-OUTVOL | InpatientGeo benchmarkPOS |
| 7 | UC Davis Medical Center | CA | 64 / 15 | $60,479,951 | 5,474 | DRG-OUTVOL | InpatientGeo benchmark |
| 8 | NYU Langone Hospitals | NY | 44 / 3 | $52,086,265 | 24,035 | DRG-OUTVOL | InpatientGeo benchmark |
| 9 | Cedars-Sinai Medical Center | CA | 31 / 5 | $48,919,830 | 14,482 | DRG-OUT | InpatientGeo benchmark |
| 10 | Santa Clara Valley Medical Center | CA | 51 / 31 | $47,456,118 | 3,270 | DRG-OUTDRG-MULTIREF | InpatientGeo benchmarkPOSCERT |
| 11 | Sinai Hospital Of Baltimore | MD | 68 / 54 | $47,329,353 | 3,607 | DRG-OUTDRG-MULTI | InpatientGeo benchmarkMAC |
| 12 | Johns Hopkins Bayview Medical Center | MD | 45 / 28 | $36,571,369 | 3,118 | DRG-OUTDRG-MULTI | InpatientGeo benchmarkCERT |
| 13 | UC San Diego Health Hillcrest | CA | 45 / 7 | $33,759,110 | 4,904 | DRG-OUT | InpatientGeo benchmark |
| 14 | Keck Hospital Of USC | CA | 24 / 7 | $30,437,855 | 1,968 | DRG-OUT | InpatientGeo benchmark |
| 15 | UCI Health-Orange | CA | 46 / 10 | $29,558,669 | 2,840 | DRG-OUT | InpatientGeo benchmark |
| 16 | Parkland Health And Hospital System | TX | 16 / 16 | $29,357,103 | 721 | DRG-OUTDRG-MULTISAFETY-NET | InpatientGeo benchmarkPOSCERT |
| 17 | Grady Memorial Hospital | GA | 46 / 25 | $24,145,203 | 2,054 | DRG-OUTDRG-MULTISAFETY-NET | InpatientGeo benchmarkPOSCERT |
| 18 | JPS Health Network | TX | 20 / 19 | $22,787,102 | 764 | DRG-OUTDRG-MULTISAFETY-NET | InpatientGeo benchmarkPOS |
| 19 | Levindale Hebrew Geriatric Center | MD | 5 / 5 | $21,697,287 | 448 | DRG-OUTDRG-CONCEN | InpatientGeo benchmarkPOS |
| 20 | Zuckerberg San Francisco General Hospital | CA | 31 / 25 | $20,812,915 | 1,149 | DRG-OUTDRG-MULTISAFETY-NET | InpatientGeo benchmarkPOS |
| 21 | Medstar Union Memorial Hospital | MD | 29 / 13 | $20,725,196 | 2,217 | DRG-OUT | InpatientGeo benchmark |
| 22 | Boston Medical Center | MA | 38 / 17 | $19,370,813 | 1,441 | DRG-OUTDRG-MULTISAFETY-NET | InpatientGeo benchmarkPOS |
| 23 | Loma Linda University Medical Center | CA | 35 / 9 | $18,971,392 | 2,662 | DRG-OUT | InpatientGeo benchmark |
| 24 | Jackson Memorial Hospital | FL | 44 / 23 | $18,358,155 | 3,966 | DRG-OUTDRG-MULTISAFETY-NET | InpatientGeo benchmarkPOS |
| 25 | University Health System | TX | 37 / 18 | $17,689,048 | 2,255 | DRG-OUTDRG-MULTI | InpatientGeo benchmark |
Tier 1 surfaces statistical anomalies. Without an agentic layer, every academic medical center on the list above looks suspicious. Ava is JIL's in-house agentic AI that reads each finding, cross-references the full 28-source backbone, groups candidates by fraud archetype, and routes each one to the cheapest Tier 2 evidence path that would substantiate or rule out the pattern. The result: instead of $200K of indiscriminate Tier 2 sweeps on 25 candidates, you get a $48K targeted plan on the 6 candidates that actually warrant it.
Ava's planner is signal-aware: it knows which fraud archetypes the 28 federal sources can corroborate, which require BAA Tier 2 data, and which can be ruled out at zero marginal cost via existing public-data signals. Each finding leaves the agent with (a) an archetype label, (b) a confidence-weighted Tier 2 plan, and (c) an explainable per-finding rationale.
Ava clusters the 25 Tier 1 candidates into 6 archetypes by signal pattern, not by hospital identity. The cluster determines what evidence is needed and where to find it.
Without Ava, every Tier 1 candidate would be funneled into a generic Tier 2 sweep. With Ava, only the candidates whose archetype warrants substantiation get a Tier 2 plan, and the plan is sized to the signal. Estimated Tier 2 cost per archetype:
For each finding, Ava queries the full 28-source backbone in parallel and synthesizes a confidence score:
For the 25 candidates above, Ava's confidence-weighted query of all 28 sources returned: 9 high-confidence rule-outs (academic), 7 high-confidence rule-outs (safety-net), 2 medium-confidence concerns (CERT match), 4 low-confidence concerns (regional system case-mix), 3 low-confidence concerns (concentration). Zero exclusion-list / enforcement matches.
Ava maps every Tier 1 signal to the federal source that produced it, the Tier 2 evidence path that would corroborate it, and the marginal cost of that path. No blind sweeps.
Pre-trained on academic / safety-net / specialty-hospital cohorts. A 6σ outlier at Stanford and a 3σ outlier at a small for-profit specialty hospital get different archetype labels and different Tier 2 routing, even at the same per-DRG payment.
For each candidate that survives rule-out, Ava picks the smallest evidence subset (records pull + interview list + targeted audit) that would substantiate the disposition - no exhaustive workup until needed.
Every disposition Ava proposes carries a citation-trail: which federal source contributed which signal, which CERT detector matched, which archetype priors fired. Same trail an appeals body or audit committee would need.
Ava walks the UBO graph (CMS Owners + PECOS) and the address-co-location graph in real time. A four-hospital chain owned by one individual and billing identical DRG patterns across three states gets one network-level finding, not four single-hospital ones.
For findings that proceed to Tier 3, Ava emits a CREB(TM) - Court Ready Evidence Bundle - anchored to CourtChain (FRE 902(14) admissible). Each bundle cites the exact federal data source, version, and effective date used to produce every conclusion.
Each Tier 2 / Tier 3 outcome flows back into Ava's archetype priors. Confirmed dispositions (substantiated, ruled-out, settled) tune the archetype thresholds for the next pass.
Today: hospitals, SNF, hospice, HHA, DMEPOS, Part D, physician E/M. The 28-source backbone covers every Medicare service line. Same Ava agent, different cohort priors.
DOJ FCA recoveries hit a record $6,800,000,000 in FY 2025, with 1,297 qui tam filings - the highest in U.S. history. JIL's in-house Tier 1 + Ava stack ran on the live federal data backbone (28 sources, 145,879 inpatient records, 27 CERT detectors, OFAC SDN, NCD/LCD, MAC jurisdictions, and 22 more) and surfaced $1.18B in cohort-level overage in under 12 seconds. Ava's archetype routing then collapsed that to a $48K Tier 2 plan on the 6 candidates that warrant substantiation.
Three things this POC demonstrates:
This POC ran on JIL's in-house ingestion of 28 federal sources. The full Tier 2 stack (bank fingerprinting, FinCEN BOI, Street View AI, ATTOM premise records) ships with the customer engagement under BAA + GLBA + per-engagement legal-basis authorization. CREB(TM) output is FRE 902(14)-anchored and reproducible in discovery.