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MongoDB Healthcare global

CentralReach

Enterprise Knowledge Search

CentralReach needed a flexible clinical data platform that could unify billions of heterogeneous records, reduce administrative burden on autism-care teams, and support natural-language retrieval over patient information.

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CentralReach processes approximately 62 million service appointments per year.
CentralReach serves more than 200,000 professionals globally who support approximately 400,000 patients.
The platform manages more than 4 billion clinical data points overall, growing by 1B+ annually.
There are only 75,000 Board Certified Behavior Analysts in the U.S. despite growing demand.

Reported impact

Business outcome

CentralReach improved clinician efficiency, reduced documentation-driven claim and continuity issues, and created a scalable Care360 foundation for future predictive analytics and AI decision support.

  • Customer Experience
  • Productivity Improvement
  • Scalability

Solution

CentralReach built its Care360 platform on MongoDB Atlas to unify clinical information for autism care delivery and make that data accessible through AI-assisted experiences. Assessments, clinical notes, treatment plans, session documentation, and related records are aggregated into the Atlas document model, which can absorb new data types and metadata as care workflows evolve. CentralReach then enriches those documents with natural-language-processing outputs and other AI-derived metadata so clinicians can query the record in natural language. MongoDB Atlas Vector Search provides the semantic retrieval layer, while LlamaIndex-powered ingestion and indexing pipelines plus gravity9 enrichment and Q&A workflows help structure unstructured content for scalable use. This creates a comprehensive, shared view of each patient that is available to clinicians, caregivers, and other members of the care team. The architecture reduces documentation friction today and gives CentralReach a foundation for future predictive and decision-support features.

Data flow

Assessments, clinical notes, treatment plans, and session records are aggregated into MongoDB Atlas, enriched with NLP metadata through ingestion pipelines, indexed for semantic retrieval with MongoDB Atlas Vector Search, and then surfaced through Care360 so clinicians and caregivers can access the right patient context quickly.

Solution architecture

3 components · 3 layers
  1. Storage
    • MongoDB Atlas Stores the unified Care360 clinical record and supports ongoing enrichment of healthcare data from many sources.
  2. Serving
    • MongoDB Atlas Vector Search Enables natural-language semantic querying across patient and clinical information.
  3. Orchestration
    • LlamaIndex Provides document-ingestion, parsing, extraction, and indexing pipelines that enhance the Atlas-based clinical data model.

Architecture clues

  • Care360 aggregates assessments, clinical documentation, and session notes into a single patient view.
  • CentralReach is exploring real-time predictive analytics and AI-driven decision support on top of the same foundation.
  • Metadata extracted from NLP and other AI techniques is added directly into the Atlas document model.
  • The architecture was optimized with help from the MongoDB AI Applications Program and partners.

Evidence from the source

Care360 aggregates data from diverse sources, including assessments, clinical documentation, and session notes.
CentralReach seamlessly integrates 4 billion monthly data points into a unified interface for providers.
“It quickly became apparent that using a document database like MongoDB was the best way to do it.”
“The MongoDB Atlas document model enables the flexibility to continually iterate and enrich the information we know.”
Transparency Evidence & methodology 70/100 reference value · 77% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value70/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact0/25
    0 metrics describe measurable impact.
  • Deployment scale5/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence77%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    4 direct source quotes retained.
  • Date provenance0/10
    The publication date has no documented provenance.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity8/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. MongoDB

    CentralReach customer story

    First-party
    https://www.mongodb.com/solutions/customer-case-studies/centralreach
    Source type
    Vendor-published customer story
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes

Date evidence

  • First archived Jul 16, 2025 · Internet Archive · Independent First Wayback Machine capture; confirms the page existed by this date, not when it was published.
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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