AWS Healthcare United States

One Medical

Customer Service Automation · Document Processing

One Medical needed to reduce administrative burden on clinicians, support secure multimodal care across the United States, and give providers faster access to patient context while maintaining privacy and compliance.

one-sixth of his or her time on administrative work
over 60 percent of American physicians reported symptoms of burnout in late 2021

Reported impact

Business outcome

One Medical delivers more consistent patient-centered care, gives clinicians more time to focus on patients instead of paperwork, and can move quickly to add new services as it scales in select US geographies.

  • Compliance
  • Customer Experience
  • Productivity Improvement
  • Scalability

Solution

One Medical built its primary-care technology platform on AWS so clinicians can spend less time on administration and more time with patients. At the center is 1Life, its purpose-built electronic health record system, which gives providers a single screen for charts, notes, medications, studies, and outside records across in-person and virtual care. The platform runs on AWS and supports secure messaging plus 24/7/365 on-demand video visits. On top of that foundation, One Medical uses HIPAA-compliant machine-learning services and Amazon Bedrock to automate document processing, explore patient-record summarization for providers, and recommend responses in telehealth messaging. Amazon SageMaker supports the broader ML environment surfaced in the case study. Together these services create a flexible, extensible care platform that shares patient context across clinics, reduces repetitive work, and lets One Medical add new services quickly as it scales.

Data flow

Patient information is captured in the 1Life platform and exposed to providers across clinics and virtual channels; AWS-hosted ML services then automate document processing, summarize records for clinicians, and suggest message responses so care teams can act on patient context more quickly.

Solution architecture

2 components · 2 layers
  1. Compute
    • Amazon SageMaker Supports the broader HIPAA-compliant machine-learning environment referenced by the case study.
  2. Serving
    • Amazon Bedrock Supports patient-record summarization and suggested responses in telehealth messaging.

Architecture clues

  • 1Life is a purpose-built electronic health record designed around One Medical's care model.
  • One Medical describes itself as an AWS-first health technology platform.
  • Uses HIPAA-compliant machine-learning and AI services on AWS.

Evidence from the source

From a single screen, providers can see all relevant information about each patient and can perform all necessary actions.
One Medical is also exploring the use of large language models to enhance human interaction in telehealth settings by recommending helpful responses to patient messages.
One Medical uses AWS services to host its healthcare platform, including 1Life, its own purpose-built Electronic Health Record (EHR) system.
Using Amazon Bedrock ... One Medical is exploring automatically summarizing patient records for providers.
the company uses machine learning solutions to automate document processing for its patients.
Transparency Evidence & methodology 70/100 reference value · 85% 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 confidence85%
  • 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 provenance8/10
    The publication date comes from publisher-controlled metadata.
  • 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. AWS

    One Medical customer story

    First-party
    https://aws.amazon.com/solutions/case-studies/one-medical-case-study/
    Source type
    Vendor-published customer story
    Published
    Jan 11, 2023
    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

Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.