// healthcare network intelligence

Network structure reveals what rate tables cannot.

Caber Analytics applies network science methods to CMS-mandated price transparency data and federal provider registry files to surface structural findings for organizations making examination, contracting, and network decisions.

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network topology · behavioral health vs. medical · sample market source: payer mrf + nppes · illustrative

// who this is for

Built for organizations that need structural clarity

Caber is designed for buyers who need to understand not just what rates exist, but how networks are actually structured -- and what that structure means for access, parity, and adequacy.

State insurance departments

Network topology and parity findings built for examination context, with traceable methodology and source documentation.

Examination teams

Structured analytical deliverables that surface MHPAEA non-quantitative treatment limitation findings from MRF and provider registry data.

Provider groups

Understand rate position and network centrality before payer conversations, renewal discussions, and contracting decisions.

Healthcare strategists

Assess reimbursement patterns and network structure by service line, geography, payer, taxonomy, and Medicare-relative benchmark.

// what we deliver

Network science applied to federal price transparency data

Our work combines CMS-mandated Transparency in Coverage files, NPPES provider registry data, geography, taxonomy, and Medicare fee schedules to produce structural findings. Every result is traceable to its source.

Network Integrity Analysis

Graph-based analysis of carrier-published provider networks against NPPES registry data. Identifies phantom listings, disconnected components, hub concentration, and structural anomalies that headcount-based reviews cannot surface. Results are documented for examination use.

network

Parity Network Analysis

MHPAEA non-quantitative treatment limitation findings derived from network topology. Compares behavioral health and medical network structure using degree centrality, community detection, and connectivity metrics. Produces defensible structural parity findings beyond rate comparisons alone.

parity

Rate Position Report

See how negotiated rates compare with publicly disclosed payer rates for comparable providers in your market. Code by code, benchmarked to Medicare, with data-backed findings for contracting and network strategy conversations.

contracting

// example questions

Questions network analysis can help answer

The output is built around specific structural questions, not generic dashboards. A typical engagement turns one or more of these into a source-traceable findings packet.

Is the behavioral health network structurally comparable to the medical network, or are there topology differences that constitute a non-quantitative treatment limitation?
Which providers appear in payer-published files but cannot be validated against federal registry data?
Where is network access concentrated in a few central providers, and what does that mean for access if those providers leave?
Which services show the widest rate variation across payers, and what does the provider-payer graph look like for those services?
Where do rates sit relative to Medicare benchmarks, and is that pattern consistent across network types?

// the methodology

Why network science surfaces what tabular analysis misses

Row-level rate analysis answers what individual rates are. Network analysis answers how a system is structured -- and structure is what determines access, parity, and adequacy in practice.

01 / source

Payer-published rate data

Every rate is sourced from CMS-mandated Transparency in Coverage files and documented by file, payer, network, code, and version.

02 / enrich

Provider registry and benchmark

Provider identity, taxonomy, and location are enriched through NPPES registry data. Rates are normalized to Medicare benchmarks to enable comparison across markets, specialties, and time.

03 / model

Graph construction and analysis

Provider-payer relationships are modeled as networks. Centrality, connectivity, and community structure are measured using established graph-theoretic methods with documented assumptions.

04 / deliver

Findings, not a platform

Deliverables are complete: structural findings with plain-English interpretation and supporting source documentation, not a subscription that requires your team to finish the analysis.

// data caveat

Powerful data, interpreted carefully

Public payer transparency files are valuable, but they are not perfect and they are not a complete picture of payment reality. Caber Analytics treats them as a source of market intelligence, documents assumptions, flags data-quality issues, and distinguishes observed rate patterns from conclusions that require additional verification.

Caber Analytics provides market intelligence based on public data sources. Our work is not legal, actuarial, or compliance advice.

// about

About the firm

Caber Analytics was founded on a straightforward observation: public data only matters when someone can interpret its structure, not just its rows.

Federal transparency rules made negotiated rates and provider networks public. But the files are massive, inconsistent, and structurally complex. Most analytical work treats them as tables. Caber Analytics treats them as networks -- because that is what they are.

The firm applies network science methods to CMS-mandated Transparency in Coverage data and federal provider registry files to produce structural findings for examination teams, state regulators, and organizations making network and contracting decisions. The combination of regulatory domain expertise and purpose-built analytical methodology is the point of differentiation.

// contact

Start with a structural question

Whether you're preparing for an examination, evaluating network adequacy, or comparing reimbursement patterns: send a note and we'll tell you whether the public data can help answer it.

marcus@caberanalytics.com