Microsoft Healthcare United States

Mount Sinai Health System

Real-time Analytics

Through its strategic partnership with Microsoft, Mount Sinai is accelerating its use of AI innovation to identify risks to patients, including malnutrition, delirium, and falls.

"Creating 'augmented intelligence' at scale is our goal, and the partnership with Microsoft Azure has been a key element in our successes thus far." Scaling to support more than 3.7 million visitors and an increase of 3 TB of data each year Mount Sinai's providers receive more than 3.7 million visits annually.
"Improved nutrition is linked to wound healing, preventing hospital readmission, reducing infection rates, and lowering the risk of death." Previously, it was only possible to predict with about 20 percent accuracy which patients were at risk of developing malnutrition.
Mount Sinai's new predictive tool can indicate which patients a dietitian should visit with over 70 percent positive predictive value, greatly reducing the number of unnecessary visits.

Reported impact

Business outcome

"Creating 'augmented intelligence' at scale is our goal, and the partnership with Microsoft Azure has been a key element in our successes thus far." Scaling to support more than 3.7 million visitors and an increase of 3 TB of data each year Mount Sinai's providers receive more than 3.7 million visits annually.

  • Cost Reduction
  • Customer Experience
  • Productivity Improvement
  • Revenue Growth

Solution

Mount Sinai Health System built its Microsoft solution to improve analytics and decision-support workflows. Through its strategic partnership with Microsoft, Mount Sinai is accelerating its use of AI innovation to identify risks to patients, including malnutrition, delirium, and falls. Azure ran model inference or heavy processing. When the organization envisioned using big data to improve patient care in 2015, all compute capacity was hosted in on-premises datacenters. The stack stayed governed and extensible on Microsoft. "Creating 'augmented intelligence' at scale is our goal, and the partnership with Microsoft Azure has been a key element in our successes thus far." Scaling to support more than 3.7 million visitors and an increase of 3 TB of data each year Mount Sinai's providers receive more than 3.7 million visits annually.

Solution architecture

1 components · 1 layer
  1. Compute
    • Azure Provides the core cloud infrastructure and managed services underlying the deployment.

Architecture clues

  • When the organization envisioned using big data to improve patient care in 2015, all compute capacity was hosted in on-premises datacenters.

Evidence from the source

"Creating 'augmented intelligence' at scale is our goal, and the partnership with Microsoft Azure has been a key element in our successes thus far." Scaling to support more than 3.7 million visitors and an increase of 3 TB of data each year Mount Sinai's providers receive more than 3.7 million visits annually.
"Improved nutrition is linked to wound healing, preventing hospital readmission, reducing infection rates, and lowering the risk of death." Previously, it was only possible to predict with about 20 percent accuracy which patients were at risk of developing malnutrition.
Mount Sinai's new predictive tool can indicate which patients a dietitian should visit with over 70 percent positive predictive value, greatly reducing the number of unnecessary visits.
The Mount Sinai Health System (Mount Sinai), an integrated medical system, is innovating at the leading edge of medical care using AI solutions on Microsoft Azure.
Through its strategic partnership with Microsoft, Mount Sinai is accelerating its use of AI innovation to identify risks to patients, including malnutrition, delirium, and falls.
Transparency Evidence & methodology 85/100 reference value · 87% 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 value85/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth15/25
    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
    3 metrics describe measurable impact.
  • Deployment scale5/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence87%
  • 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 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 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. Microsoft

    Mount Sinai Health System customer story

    First-party
    https://www.microsoft.com/en/customers/story/1646954942765735886-mount-sinai-health-system-healthcare-cloud-en-united-state
    Source type
    Vendor-published customer story
    Published
    Jun 14, 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

  • Published on source page Jun 14, 2023 · Microsoft · 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.