Normalized claim
Time: 10 x decrease
Reduced tumor board case prep time 10x in testing
Last evidence check: Jul 22, 2026
Stanford Health Care accelerates tumor board prep with autonomous AI agents
Use case typeClinical decision supportUpdated Jun 13, 2026
Stanford Health Care deployed Microsoft’s healthcare agent orchestrator built on Azure AI Foundry to automate the complex preparation process for oncology tumor boards. Autonomous agents aggregate and synthesize data from EHRs, imaging, genomics, labs, and real-world evidence, and collaborate to provide actionable reports and summaries for decision-making. Agents generate patient timelines, integrate medical literature, and identify treatment options and clinical trial matches. Clinicians access these agents through natural language in Teams and Word via Copilot, reducing preparation time from hours to minutes and freeing more time for patient care. The solution remains under research but shows significant acceleration of clinical information synthesis and multidisciplinary coordination.
- Organization
- Stanford Health Care
- Industry
- Healthcare
- Location
- United States
- Published
- May 2025
Reported outcomes
10x
timeTime & speed
Strategic outcomes
Better decisions & insightImproved multidisciplinary care coordinationEmployee experienceFreed clinicians for direct patient careNew product / capabilityGenerated actionable patient reports
Catalog median for time & speed deployments: −50% across 312 reported metrics. Compare benchmarks →
Why do we believe this?Outcome claims, sources, and evidence checks
Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks
- Customer
- Stanford Health Care
- Provider
- Microsoft
- Maturity
- Production
- Linked source
- news.microsoft.com
Stanford Health Care deployed Microsoft’s healthcare agent orchestrator built on Azure AI Foundry to automate the complex preparation process for oncology tumor boards
Customer identity supportedSource describes one deploymentMaturity supported
Primary read
Use case focus
Showing 3 of 3
- 1Tumor board preparation automation
- 2Clinical decision support
- 3Healthcare agent orchestration
- Manual tumor board preparation required hours of collating and summarizing patient data across sources
- Difficulty synthesizing clinical notes, imaging, pathology, and literature for multidisciplinary meetings
- Time-intensive documentation reduced time available for patient care
- Deployed Microsoft healthcare agent orchestrator via Azure AI Foundry
- Implemented autonomous AI agents to collect and organize data from EHR, imaging, labs, genomics, and literature
- Integrated agent interaction with M365 Copilot for natural language command and reporting
- Reduced tumor board case prep time 10x in testing
- Improved speed and quality of multidisciplinary care coordination
- Enhanced clinician focus on direct patient care rather than manual summaries
Architecture
Autonomous AI agents orchestrated through Azure AI Foundry aggregate data from multiple clinical sources (EHRs, imaging, genomics, literature), synthesize patient timelines and summaries, and interact via M365 Copilot UI in Teams/Word. Specialized modeling and agent registry. Outputs generated as Word/PowerPoint docs.
Sources & evidence8
Evidence: High85/100Evidence strength
- Customer explicitly identified
- Deployment status explicitly supported
- Independent source available
- Quantified outcome available
- Technical implementation details available
- Multiple corroborating sources available
- Recent evidence check available
- Last evidence check: Jul 22, 2026.
ExpandedExpanded
The same organization appears in newer AI deployment evidence.
- Same organization re-documented as recently as 2026.
- Cited source last checked Jun 12, 2026 — ok (0/8 broken).
Measures whether this deployment's public evidence persists — not whether the system is still in production.
Type: News ArticlePublished: May 19, 2025Publisher: news.microsoft.comEvidence: SecondaryConfidence: Low
AI-generated summary. Verify important details with the linked sources before relying on this case.
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