Atlas connects six federal and international data systems into one analytical surface. Financial, clinical, regulatory, governance, forensic, and network intelligence across every organ procurement organization, transplant center, and donor hospital in the United States — benchmarked against 93 countries worldwide.
Enter Atlas →45M+
Data Points
9,034
Entities
27,787
Connections
93
Countries Benchmarked
Capabilities
Beneish M-Score trajectories with six decomposed drivers, Benford’s Law, Mahalanobis distance, and ratio stability analysis adapted from corporate finance to non-profit healthcare cost reports. Multi-year threshold tracking cross-referenced against IRS 990 filings with automated discrepancy detection.
Cost per donor, cost per organ transplanted, organs per million population, donor yield, and referral conversion rates. Every OPO ranked against national benchmarks with CMS CALC-derived donation rates.
Automated quality checks across CMS cost reports, IRS filings, OPTN donor data, and SRTR clinical outcomes. Contradiction severity ranking with filing-structure-aware explanations. Direction-aware gap analysis at hospital and OPO scale.
27,787 edges connecting OPOs, hospitals, transplant centers, and vendors across five relationship types. Shared vendor detection, related-party transaction tracing, and cross-OPO linkage suspicion scoring.
48,718 hospital survey citations. 1,019 transplant center deficiencies. 505 OPO citations. Congressional investigation findings. FDA tissue adverse events. Misconduct registry spanning two decades.
Donation rates and transplant volumes for 93 countries across six WHO regions. Compare U.S. OPO performance against Spain, the UK, Eurotransplant, and the global frontier.
Longitudinal panel tracking every OPO across five to seven fiscal years. Cost structure decomposition, year-over-year growth rates, change point detection, agency dependency shifts, and K-means trajectory clustering with PCA and UMAP dimensionality reduction.
Five specialized agents collaborate on natural language queries. Financial forensics, regulatory compliance, clinical outcomes, network analysis, and investigative synthesis. Ask a question, get an evidence-chained answer.
Unsupervised Discovery
K-means clustering, Isolation Forest, PCA, and UMAP run nightly across every entity in the system. Beneish M-Score trajectories track financial manipulation risk over time. No pre-defined hypotheses. Five distinct risk clusters and statistical anomalies surface from the convergence of financial, regulatory, clinical, and network signals simultaneously.
14
Features per entity
5
Risk clusters identified
4
Statistical anomalies detected
Data sources
SHA-256 provenance chain on 45 million rows. 5 GB of archived source documents. No derived metric exists without a path back to its origin.
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