Why CMS is the largest ML surface in civilian federal
Precision Federal is pursuing opportunities at the Centers for Medicare and Medicaid Services. CMS is the single largest federal healthcare payer in the United States — administering Medicare (more than 66 million beneficiaries), Medicaid and CHIP (more than 80 million enrollees), and the Health Insurance Marketplaces, with annual outlays exceeding $1.5 trillion. Every line of that spend is a row in a claims table. Every row is an AI/ML opportunity: fraud detection, waste identification, quality measurement, risk adjustment, utilization review, appeals triage, coverage determination, and beneficiary analytics.
Our federal health anchor is a production ML system shipped at a federal health agency, HHS, full ATO. That delivery discipline — big federal health data, production ML, governance-first engineering — is exactly the operating mode CMS requires. See broader past performance.
CMS — Healthcare AI Program Areas — Capability Fit Score
CMS centers and data platforms we target
- CM — Center for Medicare. Fee-for-service and Medicare Advantage policy. Part A, B, C, D operations.
- CMCS — Center for Medicaid and CHIP Services. T-MSIS and state MMIS oversight.
- CPI — Center for Program Integrity. Fraud, waste, and abuse. UPIC contractors. The natural home for FWA ML.
- CCSQ — Center for Clinical Standards and Quality. QPP/MIPS, quality measurement, CMS Five Star, survey and certification.
- CCIIO — Center for Consumer Information and Insurance Oversight. Marketplace, risk adjustment in the individual market.
- CMMI — CMS Innovation Center. Alternative payment model evaluation and design.
- OIT / OEDA — Office of Information Technology / Office of Enterprise Data and Analytics. CMS IT backbone and data strategy.
Medicare Data Management (MDM), CCW, and the data asset map
CMS data is the most valuable claims asset in the United States. The main data platforms we design around:
MDM
Medicare Data Management. CMS's integrated repository of Medicare claims and enrollment, the successor architecture to legacy CMS data mart patterns.
CCW
Chronic Conditions Data Warehouse. Research-grade longitudinal Medicare, Medicaid, Marketplace, and assessment data. CCW Virtual Research Data Center (VRDC) for enclave analytics.
IDR
Integrated Data Repository. The CMS enterprise claims data warehouse.
T-MSIS
Transformed Medicaid Statistical Information System. National Medicaid claims, eligibility, and encounter data from all 50 states.
HPMS
Health Plan Management System. Medicare Advantage and Part D plan operations data.
QPP / MIPS data
clinician quality reporting.
CMS Blue Button 2.0 and BCDA
FHIR-based beneficiary and bulk claims APIs.
NCH
National Claims History file, the master Medicare FFS claims record.
Production ML at CMS means knowing which of these to query, what governance each carries, and how to design pipelines that move inside rather than across DUA boundaries.
Fraud, waste, and abuse detection ML — our strongest CMS lane
Supervised rare-event classification
Gradient-boosted and deep-learning classifiers trained on historical OIG case outcomes, UPIC referrals, and appeals data. Calibrated scoring with precision-recall tradeoffs tuned to investigator capacity.
Unsupervised anomaly detection
Isolation forests, autoencoders, and clustering over provider-level and claim-level features. Peer-group benchmarking. Effective for emerging fraud patterns where labels do not yet exist.
Graph and network analysis
Provider-beneficiary-referral graphs. Community detection for organized fraud rings. Graph neural networks for link-level anomaly scoring.
Part D opioid prescribing ML
Prescriber- and beneficiary-level opioid risk models. Intersection with a federal health agency TEDS and CDC overdose surveillance. Direct bridge from our a federal health agency past performance.
Durable medical equipment and diagnostic fraud
DMEPOS claim pattern analysis, genetic testing and molecular diagnostic fraud scoring, hospice eligibility audits.
Agentic appeals and medical review triage
LLM agents with clinical-guideline RAG over ICD-10 and NCD/LCD policy corpora. Human-in-the-loop adjudication support. See Agentic AI.
Medicare Advantage, risk adjustment, and RADV
Medicare Advantage covers more than half of eligible Medicare beneficiaries. The risk adjustment payment system — HCC models applied to encounter data — is simultaneously the largest single line of Medicare spending adjustment and one of the most audit-scrutinized. AI/ML scope across MA includes:
Encounter data quality ML
submission completeness and accuracy modeling.
HCC code extraction NLP
clinical documentation to HCC code mapping with audit trail.
RADV audit support
Risk Adjustment Data Validation sampling and chart review ML.
Star Ratings analytics
quality measure prediction and intervention targeting.
MA plan behavior modeling
outlier detection in enrollment, disenrollment, and prior authorization patterns.
Claims data at scale — engineering, not just modeling
CMS claims data pushes the boundaries of what most small ML shops can handle. The NCH file alone is measured in tens of terabytes per year. T-MSIS is larger. Production ML here is an engineering problem first, a modeling problem second. Our relevant stack:
Lakehouse architectures
Parquet and Delta/Iceberg over S3 or Azure Data Lake, partitioned and Z-ordered for claims-table access patterns.
Distributed compute
Spark and Ray for feature engineering, Dask for Pandas-compatible workloads, GPU training where warranted.
Columnar feature stores
for provider, beneficiary, claim, and episode-level features reusable across models.
Temporal modeling
time-aware splits for claims data with eligibility gaps and retroactive adjustments.
See Data Engineering and Cloud Architecture.
Governance: ARS, CCW DUAs, and FISMA High
CMS runs many systems at FISMA High. Claims data at rest carries CMS Acceptable Risk Safeguards (ARS) controls that extend NIST 800-53. Research-grade data access comes with CCW DUAs that enumerate allowed purposes and prohibited disclosures. We design around:
ARS 5.x controls
for system security plans and continuous monitoring.
CCW VRDC enclave execution
where exfiltration is not permitted.
Minimum necessary and cell suppression
on reporting outputs.
FedRAMP Moderate and High
cloud baselines where CMS workloads require.
HIPAA and HITECH
as the non-negotiable floor.
Vehicles and pathways into CMS
SPARC
Strategic Partners Acquisition Readiness Contract. The primary CMS IT services IDIQ.
ESD / ESD-NextGen
CMS Enterprise Systems Development. Major IT program vehicle.
MIDAS
Medicare and Medicaid data analytics support.
ADVANCE 2.0
clinical and quality analytics support.
CMS Small Business IDIQs
specific small business on-ramps.
State MMIS modernization
subcontracting to MMIS modernization primes in multiple states.
HHS SBIR
CMS participates on Medicare and Medicaid AI topics.
Most of these are currently reachable for us through subcontracting to prime holders. Direct prime work requires past performance we are building, and SBIR is the cleanest self-service door.
Subcontracting, teaming, and honest positioning
We do not claim CMS past performance. We claim adjacent federal health past performance — federal health agency production ML under full ATO, federal health IT data platform delivery, multi-agency cloud migration through prior consulting employers. For CMS-specific scope, our most efficient entry paths are:
Subcontract to a SPARC or ESD prime
on a task order with AI/ML scope.
Pair with a CMS-experienced prime
on a new IDIQ or single-award competition.
Prime on SBIR
where topic fit is clear.
State MMIS subcontracting
where Medicaid analytics scope is AI/ML-intensive.
How to engage on a CMS requirement
Email bo@precisionfederal.com with the CMS center, vehicle, and scope. We respond within 24 hours with a fit assessment and teaming construct. For related pages see Machine Learning and SBIR Partnering.