NVIDIA Healthcare Germany

Clinomic

Document Processing · Real-time Analytics

Clinomic needed to consolidate ICU patient data, reduce documentation burden, and run bedside analytics and telemedicine functions without relying on cloud connectivity that could introduce latency or operational risk inside the hospital.

143% increase in patient-care capacity
26% reduction in transfers to other hospitals
33% reduction in perceived workload
550 hours saved per year per bed

Reported impact

Business outcome

Clinomic reduced documentation errors by 68 percent, saved 550 hours per year per bed, increased patient-care capacity by 143 percent, and reduced hospital transfers by 26 percent while giving care teams a real-time bedside assistant for ICU operations.

  • Customer Experience
  • Productivity Improvement
  • Risk Reduction
  • Scalability

Solution

Clinomic built Mona as a bedside intensive-care assistant on NVIDIA Jetson AGX Xavier so data aggregation, documentation support, diagnostics, and telemedicine could run directly in the patient room. The platform integrates third-party and AI applications, aggregates vital signs, lab results, demographics, and imaging inputs, and performs real-time edge processing so hospitals do not depend on cloud connectivity for core ICU workflows. NVIDIA TensorRT accelerates deployed models on Jetson, while the device's support for TensorFlow, PyTorch, and ONNX helps Clinomic integrate pretrained models with monitoring and imaging systems already used in care environments. This edge architecture powers voice recognition, multilingual translation, ultrasound enhancement, predictive analytics for conditions such as sepsis or acute kidney injury, and remote consultations from one modular clinical workstation.

Data flow

Vitals, lab results, demographics, voice commands, and imaging signals are aggregated on Mona at the bedside. Jetson AGX Xavier processes those inputs locally, TensorRT-optimized models generate documentation, predictions, translations, and image enhancements, and the resulting insights are presented to care teams or remote specialists without a cloud round trip.

Solution architecture

2 components · 2 layers
  1. Compute
    • NVIDIA Jetson AGX Xavier Provides the edge-compute platform that runs Mona's bedside data aggregation, voice, imaging, and predictive-analytics workloads in the ICU.
  2. Serving
    • NVIDIA TensorRT Accelerates deployed models so Mona can deliver real-time bedside inference with lower latency and better reliability.

Architecture clues

  • Jetson integrates pretrained models with existing monitoring and imaging systems
  • Mona performs real-time edge processing instead of relying on cloud connectivity
  • The platform is modular, open, and integrates with third-party clinical applications
  • The platform supports voice recognition, translation, predictive analytics, and telemedicine from one bedside station

Evidence from the source

68% reduction in documentation errors, enhancing the accuracy of patient records and improving overall care quality.
Its real-time edge processing eliminates reliance on cloud connectivity, delivering faster, more reliable performance in hospitals.
Mona is more than a device-it's a modular, open, and scalable platform that integrates with third-party and AI applications.
Powered by NVIDIA Jetson AGX Xavier, Mona brings telemedicine directly to the bedside.
Transparency Evidence & methodology 100/100 reference value · 84% confidence · 3 sources

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
    5 metrics describe measurable impact.
  • Deployment scale10/10
    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 traceability25/25
    12 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 transparency9/10
    3 source references 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. NVIDIA

    Clinomic customer story

    First-party
    https://www.nvidia.com/en-us/case-studies/clinomic/
    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
  2. Clinomic

    Legal notes - Clinomic

    First-party
    https://www.clinomic.ai/imprint/

    Clinomic Group GmbH, Forckenbeckstraße 66, 52074 Aachen.

    Source type
    Official company website
    Supports
    • Region
  3. Prospeo

    Clinomic Overview, Address & Contact

    Independent secondary
    https://prospeo.io/c/clinomic

    The page labels Clinomic as '51-100 Employees' and its Organization JSON-LD sets numberOfEmployees value 51.

    Source type
    Structured company profile
    Supports
    • Company size

Date evidence

  • First archived Feb 9, 2026 · Internet Archive · Independent First Wayback Machine capture; confirms the page existed by this date, not when it was published.
    Open date source
  • Sitemap last modified Aug 25, 2026 · NVIDIA · Publisher metadata Publisher-reported modification timestamp; not a publication date.
    Open date source
Not separately sourced Industry. Treat these normalized attributes as lower-confidence until a dedicated source is added.