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OpenAI Healthcare North America

Color Health

Document Processing

1. It extracts, processes, and normalizes patient information, such as family history and individual risk factors, along with clinical guidelines and data from trusted sources.

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2. Using this data, it answers key questions like, "What screenings should the patient be doing?" to identify missing diagnostics and generate a personalized screening plan.
Color Health(opens in a new window) is working with OpenAI to pioneer a new way of accelerating cancer patients’ access to treatment. Their new copilot application uses GPT‑4o to identify missing...
Color has been working to improve access to healthcare for a decade, serving more than 7 million patients since it was founded. In 2023, they partnered with the American Cancer Society to help...
Screening, diagnosis, and treatment for cancer is notoriously complex and time-consuming. And every delay makes a difference: patients whose treatments are delayed by just four weeks face a 6–13%...

Reported impact

Business outcome

Color Health(opens in a new window) is working with OpenAI to pioneer a new way of accelerating cancer patients’ access to treatment. Their new copilot application uses GPT‑4o to identify missing diagnostics and create tailored workup plans, enabling healthcare providers to make evidence-based decisions about cancer...

  • Customer Experience
  • Productivity Improvement
  • Risk Reduction

Solution

1. It extracts, processes, and normalizes patient information, such as family history and individual risk factors, along with clinical guidelines and data from trusted sources. The Color team was particularly impressed with GPT‑4o’s ability to extract and normalize information that was buried within pages of inconsistently structured and phrased information, often in different formats, such as with PDFs or clinical notes. Color’s copilot is helping clinicians create customized, comprehensive plans to start cancer treatment Color Health(opens in a new window) is working with OpenAI to pioneer a new way of accelerating cancer patients’ access to treatment.

Data flow

Color’s copilot is helping clinicians create customized, comprehensive plans to start cancer treatment Color Health(opens in a new window) is working with OpenAI to pioneer a new way of accelerating cancer patients’ access to treatment. Their new copilot application uses GPT‑4o to identify missing diagnostics and create tailored workup plans, enabling healthcare providers to make evidence-based decisions about cancer screening and treatment.

Solution architecture

2 components · 1 layer
  1. Compute
    • OpenAI API Embeds model capabilities inside the customer’s application or internal workflow.
    • GPT-4o Extracts, interprets, and reasons over multimodal or unstructured inputs inside the workflow.

Architecture clues

  • Color Health(opens in a new window) is working with OpenAI to pioneer a new way of accelerating cancer patients’ access to treatment.
  • Color’s copilot is helping clinicians create customized, comprehensive plans to start cancer treatment
  • Embeds model capabilities inside the customer’s application or internal workflow.
  • Extracts, interprets, and reasons over multimodal or unstructured inputs inside the workflow.

Evidence from the source

1.
Color Health(opens in a new window) is working with OpenAI to pioneer a new way of accelerating cancer patients’ access to treatment.
Color’s copilot is helping clinicians create customized, comprehensive plans to start cancer treatment
Transparency Evidence & methodology 81/100 reference value · 84% 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 value81/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth20/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity16/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    2 metrics describe measurable impact.
  • Deployment scale0/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence84%
  • 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 evidence15/20
    3 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. OpenAI

    Color Health customer story

    First-party
    https://openai.com/index/color-health/
    Source type
    Vendor-published customer story
    Published
    Jun 17, 2024
    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 Jun 17, 2024 · OpenAI · 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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