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Vestre Viken Hospital Trust

Vestre Viken faced mounting diagnostic workloads from rising imaging volumes, an aging population, and increasing case complexity, while radiologists were under pressure to deliver timely, accurate reports without compromising quality of care.

13,800 emergency consultations prevented
3,100 physician hours freed up — equivalent to 1.9 full-time clinical roles
594 days of waiting time saved
74,000 patients have been examined with AI support

Reported impact

Business outcome

The integrated AI workflow standardized trauma imaging across four hospitals, shortened wait times, reduced unnecessary emergency consultations, freed physician capacity, improved radiologist sensitivity, and gave frontline clinicians faster access to decision support for patient triage.

  • Customer Experience
  • Productivity Improvement
  • Risk Reduction
  • Scalability

Solution

Vestre Viken implemented an AI-enabled radiology workflow by integrating Philips AI Manager with its existing Philips Vue PACS and Philips Radiology Information System (RIS) across all four hospitals. Imaging studies, especially trauma X-rays, remain in the established RIS-PACS environment, while AI Manager acts as the central orchestration layer that connects, deploys, monitors, and governs multiple AI applications. The first production algorithm was Gleamer's BoneView, which analyzes X-rays for fractures, effusions, bone lesions, and dislocations. AI-generated findings are delivered directly into the radiologist's primary reading environment instead of a separate tool, reducing context switching and supporting faster diagnosis. The same integrated workflow also exposes preliminary AI findings to emergency and orthopedic teams, enabling 24/7 standardized triage, consistent quality across sites, and centralized governance aligned with European medical device standards.

Data flow

Trauma X-ray studies move through Vestre Viken's existing RIS-PACS workflow, where Philips AI Manager orchestrates Gleamer's BoneView on the images. BoneView generates findings that are injected back into Philips Vue PACS and RIS, allowing radiologists, emergency teams, and orthopedic teams to review AI-supported results in their normal reading and triage workflow.

Solution architecture

4 components · 4 layers
  1. Compute
    • Gleamer's BoneView Analyzes X-rays to detect fractures, effusions, bone lesions, and dislocations for routine clinical decision support.
  2. Storage
    • Philips Vue PACS Provides the primary imaging and reading environment where AI-generated insights are surfaced to radiologists.
  3. Orchestration
    • Philips AI Manager Central orchestration platform that connects, deploys, monitors, and governs AI applications within the radiology workflow.
  4. Uncategorized
    • Philips Radiology Information System (RIS) Integrates AI assistance into the hospital's radiology workflow and makes findings available inside the RIS-PACS environment.

Architecture clues

  • AI findings are delivered directly into the primary reading environment
  • Deployment emphasizes data governance and compliance with European medical device standards
  • Norway's first hospital trust to integrate Philips AI Manager with Philips Vue PACS and RIS
  • Platform is used to centrally manage multiple AI algorithms
  • Single workflow available 24/7 across sites
  • Workflow standardized across all four hospitals

Evidence from the source

"13,800 emergency consultations prevented"
"3,100 physician hours freed up — equivalent to 1.9 full-time clinical roles"
"594 days of waiting time saved"
"Local validation at Vestre Viken showed AI accuracy ~91% (radiologists 95%); with AI assistance, radiologist sensitivity increased from 93% to 98%"
"Since going live with the Gleamer BoneView Application in August 2023, 74,000 patients have been examined with AI support"
"The first solution deployed was Gleamer's BoneView, Norway's first AI tool in routine clinical use, trained to detect fractures, effusions, bone lesions and dislocations on X-rays of patients over two years old."
"Through deep integration with Philips Vue PACS and Philips Radiology Information System (RIS), AI Manager delivers AI-generated insights directly into the radiologist's primary reading environment."
"Vestre Viken Hospital Trust implemented an AI-enabled radiology workflow by integrating Philips AI Manager with existing Philips Vue PACS and RIS."
Transparency Evidence & methodology 100/100 reference value · 95% 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 value100/100
  • 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 impact25/25
    4 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence95%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability25/25
    12 of 12 key fields link to a named source.
  • Direct evidence20/20
    8 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency10/10
    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. Philips

    AI-powered radiology workflow - Philips

    First-party
    https://www.philips.com.sg/healthcare/customer-story/ai-radiology-workflow-vestre-viken-ris-pacs
    Source type
    Vendor-published customer story
    Published
    Feb 18, 2026
    Supports
    • Customer
    • Industry
    • Region
    • Company size
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • Publication date
    • evidence quotes

Date evidence

  • Published on source page Feb 18, 2026 · Vestre Viken Hospital Trust · First-party
    Open date source