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Microsoft Healthcare United States

Ontada

Data Lake / Data Warehouse Modernization · Document Processing · Enterprise Knowledge Search

It is time-consuming, error-prone, and cost-prohibitive for Ontada to generate key insights using manual processes to analyze 150 million unstructured oncology documents.

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It is time-consuming, error-prone, and cost-prohibitive for Ontada to generate key insights using manual processes to analyze 150 million unstructured oncology...
Ontada now accesses an estimated 70% of previously unanalyzed or unused information
Ontada now accesses an estimated 70% of previously unanalyzed or unused information and has accelerated its life science product development, speeding up time...
Sagran Moodley, SVP and Chief Innovation Technology Officer, Ontada Analyzes estimated 70% of previously buried data Product time-to-market takes one week, not...

Reported impact

Business outcome

Knowing each data point can make a life-changing difference for cancer patients.

  • Compliance
  • Productivity Improvement
  • Revenue Growth
  • Risk Reduction

Solution

Ontada modernized its analytics estate on Azure OpenAI Service Batch API, Azure OpenAI Service, Azure AI Foundry, moving from fragmented legacy tooling to a governed cloud data platform. It also connects with surrounding systems such as iKnowMed, which lets the new Azure-based workflow fit into existing operations rather than standing apart from them. Knowing each data point can make a life-changing difference for cancer patients. The architecture emphasizes managed services, reusable integrations, and governed data handling so the organization can scale adoption without rebuilding the solution for each new team or use case.

Data flow

Content and operational context are prepared in Azure AI Document Intelligence, passed to Azure OpenAI for generation or extraction, stored in Azure storage services, and returned through the user-facing application to employees or customers with grounded responses.

Solution architecture

5 components · 3 layers
  1. Ingest
    • Azure AI Document Intelligence Extracts text, layout, and document fields before downstream AI or analytics processing.
  2. Compute
    • Azure OpenAI Service Batch API Processes large document batches asynchronously to extract structured entities at scale.
    • Azure OpenAI Service Runs large language model inference for summarization, extraction, conversational AI, and grounded answers.
    • Azure AI Services Supplies prebuilt AI capabilities such as language, speech, or vision services within the application flow.
  3. Orchestration
    • Azure AI Foundry Provides the managed environment for model orchestration, experimentation, and responsible AI operations.

Architecture clues

  • Extracts text and document structure prior to downstream AI processing.
  • Uses Azure OpenAI for grounded generation or large-scale extraction workloads.

Evidence from the source

"The Azure OpenAI Service Batch API enables us to apply an elevated rigor and unparalleled computing power, delivering exceptional data quality, accuracy, and deep insights into the longitudinal patient journey," says Wanmei Ou, PhD, Vice...
It is time-consuming, error-prone, and cost-prohibitive for Ontada to generate key insights using manual processes to analyze 150 million unstructured oncology documents.
Knowing each data point can make a life-changing difference for cancer patients.
Using Microsoft Azure AI Foundry and Azure OpenAI Service, Ontada implemented large language models to target nearly 100 critical oncology data elements across 39 cancer types, delivering access to quality data from unstructured oncology...
Using Microsoft Azure AI Foundry and the Azure OpenAI Service Batch API to accelerate innovation, Ontada implemented large language models to target nearly 100 critical oncology data elements across 39 cancer types, significantly...
Transparency Evidence & methodology 92/100 reference value · 89% 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 value92/100
  • 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 specificity12/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    5 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence89%
  • 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
    5 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    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. Microsoft

    Ontada customer story

    First-party
    https://www.microsoft.com/en/customers/story/19761-mckesson-azure-open-ai-service
    Source type
    Vendor-published customer story
    Published
    Feb 4, 2025
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

  • Published on source page Feb 4, 2025 · Microsoft · First-party
    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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