Deploying AI in Healthcare

2026-03-04 · Source: The a16z Show · Field: Health & Wellbeing — Medical Devices & Health Technology, Healthcare Systems & Policy, Artificial Intelligence & Machine Learning · Depth: Intermediate, extended

Summary

Ambience Healthcare, led by CEO Nikhil Buduma, is transforming clinical workflows by deploying AI in healthcare, addressing the rising demand for care and clinician burnout. The company's unique approach involved first running a medical practice to deeply understand operational challenges before building its platform. Ambience now works with major academic medical centers, achieving over 75% daily clinician adoption and projecting significant financial returns, such as one health system's anticipated \$30 million in net new margin. This success stems from an infrastructure layer that integrates with EHRs, enabling rapid AI product development and tackling complex "last mile" problems like data context, quality definition, and deep client relationships, despite the rapid evolution of AI capabilities.

Key takeaway

For hospital system CEOs evaluating AI investments, prioritize solutions demonstrating high clinician adoption and clear financial ROI, not just marketing. Your organization needs AI that integrates deeply with existing EHRs to create new data layers and accelerate product development. Focus on partners willing to commit to measurable operating margin improvements, as this will unlock a flywheel of investment, talent attraction, and patient volume, securing your system's future.

Key insights

AI is transforming healthcare by improving clinical workflows and generating significant operating margins, despite complex data and quality definition challenges.

Principles

• AI clock speed differs from product clock speed.
• Deep relationships are crucial for rapid deployment.
• New data layers are essential for domain-specific AI.

Method

Ambience Healthcare's method involves building an infrastructure layer atop EHRs to pull and groom data, enabling rapid AI product development and addressing last-mile problems in clinical intelligence and revenue cycle management.

In practice

• Prioritize clinician adoption and utilization.
• Focus on AI solutions that demonstrably improve operating margin.
• Build data infrastructure to support rapid AI product iteration.

Topics

• Healthcare AI
• Clinical Workflows
• EHR Integration
• Operating Margin
• AI Adoption
• Ambience Healthcare

Best for: Executive, Entrepreneur, Director of AI/ML, AI Product Manager, Consultant

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Editorial summary, takeaway, and curation by AIssential. Original article published by The a16z Show.