Using Confidential Computing to Cure Cancer

BeeKeeperAI, Launch, and UCSF CDHI teamed up to accelerate healthcare AI by designing core architecture, gathering user insights, and building a prototype—cutting years and costs from development.

Results that matter:
Secure, Scalable Architecture
Enables healthcare AI developers to train algorithms without compromising patient data.
Roadmap to Future
Developed a prototype and product roadmap to guide future developments and align stakeholders.
Quick Look
Services
Architecture Design
Prototype Development
UX/UI Design
Azure AI Services
Partner
Microsoft
Key Technologies
Azure SQL Database
Azure Data Lake
Azure Machine Learning

The Challenge

Accelerating healthcare AI by 1000x

In today’s environment, it takes approximately 3 years and $5M for an algorithm to achieve generalizability and be viable for FDA approval. Healthcare AI development is a slow process that is burdened by finding realistic data to train on and facilitating secure access to that data that won’t compromise patient safety.

Launch helped accelerate the healthcare AI development timeline by laying the groundwork for secure, scalable algorithm training—cutting time to market and cost by orders of magnitude.

The Solution

Envisioning the future of healthcare AI

BeeKeeperAI brings confidential computing in the cloud to enable secure collaboration between algorithm developers and healthcare data sets. AI will revolutionize healthcare as we know it – but in order for this to happen, we need more developers training de novo algorithms more often.

Launch worked with University of California, San Francisco's Center for Digital Health Innovation (CDHI) to lay the foundational system architecture and design key elements of the user experience in order to bring it to market. This included:

  • Product research with key user groups
  • Clickable prototype and product roadmapping
  • Clinical data integration
  • Demonstrate key architecture solutions

Product research: Insights from real world experiences

Our client had a hunch based on their experience that the value proposition of their product was sound, but needed further validation to help refine the product offering and their pitch. We conducted 15 one-hour long interviews with key subject matter experts and user groups. We synthesized our findings and distilled our learnings into artifacts to guide our design and architecture phases.

  • Key Insights – takeaways and learnings from research
  • Design Guidelines – what user-centered concepts will guide the product's development

Setting the system up for success

  • Moving from high level concepts to specific implementation details, there were foundational pieces of the system that needed to be systematically evaluated and designed.
  • We worked to identify core platform needs for functionality and identify solutions for these.
  • Tackling some of the toughest problems of the system first and working through prototypes, we used engineering spikes to unblock the team and test our assumptions about the system.

The Results

  • 15 in-depth user interviews synthesized into actionable insights.
  • Clear architectural blueprints covering data security, system entities, and workflows.
  • A working prototype demonstrating the user experience and product roadmap.

Launch helped accelerate the healthcare AI development timeline by laying the groundwork for secure, scalable algorithm training—cutting time to market and cost by orders of magnitude.

Envisioning a future experience

Based on our research – we identified key features, user needs, and system requirements. These set the foundation for the product and helped us define the information architecture and service blueprint. We then created a clickable prototype that follows one key user story.

It answers:

  • What will the experience be like? 
  • What features and functions will the system have?
  • How will it organize information?

The prototype embodies a product roadmap and casts a vision for BeeKeeperAI as a service 2-3 years in the future.

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