Total Spending
$1.09T
Full T-MSIS dataset
Total Providers
617,503
Unique billing NPIs
Billing Records
227.1M
Jan 2018 – Dec 2024
Flagged Providers
0
In sample dataset
Flagged Spending
$0
At-risk amount
Prescribing Alerts
0
Opioid/controlled flags

Annual Spending Trend

2018–2024

Top 10 States by Spending

Sample

Risk Distribution

Geographic Risk Density

Top 10 States

Recent Alerts

View All →
Provider NPI State Specialty Total Billed Claims Risk Flags
Back to Search
0

Provider Name

Critical Organization
NPI: 0000000000
Specialty
City, ST
Total Billed
$0
Total Claims
0
Beneficiaries
0

Monthly Spending Trend

Year-over-Year Comparison

Top HCPCS Codes

Code Description Claims Total Paid Cost/Claim Median Ratio

Peer Benchmarking

vs Specialty Peers

Beneficiary Demographics

Affiliations / Network

Fraud Test Results

15 tests

Critical

0

High

0

Elevated

0

Total Flagged

0
Provider State Specialty Total Billed Risk Score Flags Affil. Flagged Flag Details
Total Spending
LowHigh
Providers Shown0
Total Flagged0
At-Risk Spending$0
Cross-State Networks0
Back to States

State Name XX

Total Spending
$0
Providers
0
Flagged
0

Spending by Year

Top Specialties

Top Providers

ProviderSpecialtyTotal BilledRiskFlags

Top Procedures

CodeDescriptionTotal PaidClaims

Top 10 HCPCS Codes by Spending

Code Category Description Total Spending Total Claims Avg Cost/Claim Providers
Clusters Found
0
Total Providers
0
High-Risk Clusters
0
Combined Spending
$0
Cluster Members State Total Spending Shared Beneficiaries Flagged Members Risk Level

Controlled Substance % by Top Prescribers

Opioid Prescribing Rate by Specialty

Provider Specialty State Total Rx Controlled % Opioid % Risk

Spending by Ownership Type

Risk Distribution by Ownership

Corporate Chain Spotlight

ProviderSpecialtyStateTotal BilledRiskFlags
🛡️
SOC 2 Type II
Certified
🔐
AES-256
Encryption at Rest & Transit
📋
NIST 800-53
Control Framework
🏛️
FedRAMP
Roadmap Initiated

System Overview

IMX-Ray™ is a Fraud Detection Decision Support System (FDSS) designed for federal and state agencies responsible for Medicaid program integrity. The system applies advanced statistical analytics, machine learning, and real-time data integration to identify anomalous billing patterns, high-risk providers, and potential fraud, waste, and abuse (FWA) across the Medicaid ecosystem.

Data Architecture

IMX-Ray™ is powered by IMX Data's proprietary healthcare claims repository, one of the largest commercially available datasets in the United States:

  • 85+ billion healthcare claims processed
  • 300+ million unique patient records
  • $1.09 trillion in Medicaid expenditures analyzed (T-MSIS 2018–2024)
  • 617,503 unique billing provider NPIs tracked
  • 227 million Medicaid billing records in current analysis set
  • 9 live CMS data feeds integrated for real-time enrichment

Primary data sources include the HHS T-MSIS (Transformed Medicaid Statistical Information System), CMS NPPES NPI Registry, CMS-64 State Expenditure Reports, OIG LEIE Exclusion Database, Open Payments, Hospital Compare, Nursing Home Compare, and State Drug Utilization data.

Analytical Capabilities

The system employs 15 validated statistical tests organized across six detection categories:

  • Volume Anomaly Detection: Identifies providers with statistically significant deviations in claims volume, beneficiary counts, and billing frequency relative to specialty and geographic peers.
  • Cost Anomaly Detection: Flags outlier reimbursement patterns including per-claim cost deviations, rate anomalies, and disproportionate spending relative to service mix.
  • Pattern Recognition: Detects suspicious billing patterns including single-code concentration, temporal consistency anomalies, Benford's Law violations, and systematic upcoding indicators.
  • Growth Anomaly Detection: Identifies explosive billing growth, rapid volume escalation, and new-entrant risk patterns commonly associated with fraudulent operations.
  • External Reference Matching: Cross-references OIG Exclusion List, revoked provider databases, and enforcement action histories.
  • Prescribing Analytics: Monitors controlled substance and opioid prescribing rates against specialty benchmarks and geographic norms.

Risk Classification Framework

Each provider receives a composite Fraud Risk Score (0-100) based on the weighted severity and count of triggered detection flags. Classification tiers align with investigative prioritization:

  • Critical (70-100): Immediate investigation recommended. Multiple severe flags indicating high probability of billing anomalies.
  • High (40-69): Priority review. Several concerning indicators warranting detailed examination.
  • Elevated (15-39): Monitoring recommended. Flags present but lower severity; may reflect legitimate practice variations.
  • Standard (0-14): No significant anomalies. Billing patterns within expected parameters.

Important: Risk scores are investigative leads, not determinations of fraud. All flagged providers require human review by qualified investigators before any enforcement action.

Investigation Support Features

  • Provider Deep-Dive: Comprehensive profiles including billing history, HCPCS code analysis, peer comparison, and risk factor breakdown
  • Network Analysis: Maps provider affiliations and shared beneficiary patterns to identify coordinated fraud schemes
  • Geographic Intelligence: Heat-map visualization of fraud density by state and region with drill-down capability
  • Case Study Library: Documented analysis of major fraud schemes (Operation Gold Rush, Wound Care Networks, MA Upcoding) with detection methodology
  • Real-Time CMS Integration: Live data feeds from 9 CMS endpoints for current provider verification and enrichment
  • PDF Report Generation: Export investigation-ready fraud risk assessments with full provider analysis and supporting data

Deployment Options

Configuration Description Data Scope
SaaS, Multi-tenant Cloud-hosted, IMX-managed infrastructure. SOC 2 certified. Full 85B+ claims dataset
SaaS, Dedicated Isolated tenant with dedicated compute and storage. Full dataset + custom feeds
On-Premises Deployed within agency network boundary. FedRAMP-aligned. Agency-specified scope
GovCloud AWS GovCloud (US) or Azure Government deployment. Full dataset, ITAR-compliant

Data Sources & Integrations

HHS T-MSIS Medicaid Spending Dataset

227 million records, $1.09 trillion, 617,503 NPIs. January 2018 through December 2024. Released February 2026 by the Department of Health and Human Services.

CMS NPPES NPI Registry

Real-time provider identity verification. NPI-validated provider records including names, addresses, specialties, and organizational affiliations via the National Plan and Provider Enumeration System.

CMS-64 State Expenditure Reports

State-level Medicaid spending calibration. FY 2023 quarterly financial data for geographic distribution weighting and state-level aggregate validation.

OIG LEIE Exclusion Database

Office of Inspector General List of Excluded Individuals/Entities. Real-time cross-referencing against known sanctioned providers and excluded entities.

About IMX Data

IMX Data (imxresearch.com) operates one of the largest commercially available healthcare claims databases in the United States, processing over 85 billion claims representing 300+ million patients. The company provides data analytics, fraud detection, and healthcare intelligence solutions to federal agencies, state Medicaid programs, health plans, and research institutions.

IMX Data holds SOC 2 Type II certification and maintains security controls aligned with NIST SP 800-53. FedRAMP authorization is in progress.

For inquiries: imxresearch.com · This system contains evaluation data. Contact IMX Data for production deployment with full 85B+ claims dataset access.

Email

jim@imxresearch.com

For demos, pricing, and partnership inquiries

Website

www.imxresearch.com

Company information and data products

Request a Demo or Proposal

Complete the form below and we will respond within one business day.

Deployment Options

☁️
SaaS
Cloud-hosted, IMX-managed
🏛️
GovCloud
AWS GovCloud / Azure Gov
🔒
On-Premises
Within agency network boundary