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PSV

Risk Management

PSV wanted earlier warning of player overload and upper respiratory infection because infections can spread before symptoms appear and traditional sports monitoring focuses on what has already happened rather than what is likely to happen next.

163 different biomarkers
2.3 days before a positive test
40 to 45 percent of COVID-19 transmissions occur in this early, asymptomatic phase
9,381 DoD personnel

Reported impact

Business outcome

PSV is testing the system to identify overload or infection earlier, enabling faster intervention through training-load adjustments or earlier medical checks, with the goal of preventing illness-related disruption and optimizing recovery.

  • Risk Reduction

Solution

PSV is piloting Philips' patented RATE algorithm as a predictive health-monitoring workflow for elite football operations. In the implementation described, player biometric data from wearables is the ingestion layer: Philips analyzes subtle changes in those signals to detect patterns that may precede overload or upper respiratory infection before symptoms are visible. The main compute component is the RATE algorithm, which generates early-warning risk indications rather than retrospective reports. Those outputs are served to PSV's medical staff so they can intervene sooner by adjusting training load or initiating an earlier medical check. The page does not identify a named storage platform or orchestration product, but it does explain that the algorithm lineage was previously developed on Philips infection datasets and later adapted to commercial off-the-shelf wearables, giving the PSV deployment a validated model foundation that Philips is further refining through this live sports use case.

Data flow

Player biometric data from wearables is captured before symptoms appear and fed into Philips' RATE algorithm, which analyzes subtle physiological changes and produces early-warning signals for overload or infection. PSV medical staff review those outputs and can respond by adjusting training load or ordering an earlier medical check, while Philips uses the deployment to further refine the model.

Solution architecture

1 components · 1 layer
  1. Compute
    • RATE algorithm Analyzes wearable-derived physiological signals to identify early signs of overload or upper respiratory infection so PSV staff can intervene sooner.

Architecture clues

  • A wearable version of the algorithm was validated during COVID-19 using commercial-off-the-shelf wearables in a cohort of 9,381 DoD personnel.
  • Philips describes RATE as a patented algorithm for recognizing early signals of upper respiratory infection.
  • The deployment uses wearable data as the operational signal source.
  • The first version of RATE was developed in 2019 using machine learning and trade-space analyses across demographics and 163 biomarkers from more than 36,000 hospital-acquired infection cases.

Evidence from the source

"A 2022 study ... showed how, in a cohort of 9,381 DoD personnel, the algorithm was able to detect COVID-19 infection on average 2.3 days before a positive test and up to six days prior."
"Historically data in sports focuses on what has already happened with this technology the medical staff can focus on what is going to happen and keep players on peak performance."
"PSV is the first sports team in the world to use advanced AI technology from Philips to predict upcoming overload or infections."
"The first version of the RATE algorithm, developed in 2019, used large-scale data science, machine learning, and trade-space analyses across demographics and 163 different biomarkers ... from a rich Philips data set of over 36,000 cases of hospital-acquired infections."
"The innovation being tested at PSV ... is based on a Philips-developed and patented algorithm (the RATE algorithm) that can recognize early signals of (upper) respiratory infections using data from wearables."
"This would allow us to intervene sooner, for example by adjusting training load or performing a medical check earlier. AI helps us recognize these signals earlier in the data from players' wearables."
Transparency Evidence & methodology 90/100 reference value · 91% 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 value90/100
  • 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 impact15/25
    1 metric describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence91%
  • 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
    6 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

    Philips AI technology helps predict the health of PSV football team | Philips

    First-party
    https://www.philips.com/a-w/about/news/archive/standard/news/press/2026/philips-ai-technology-helps-predict-the-health-of-psv-football-team.html
    Source type
    Vendor-published customer story
    Published
    Jan 16, 2026
    Supports
    • Customer
    • Region
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Business outcome
    • Metrics
    • Architecture
    • Publication date

Date evidence

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