Should Sana prioritize expanding its AI-powered medical diagnosis tool to more specialties or focus on improving accuracy for existing covered conditions?

Introduction

The trade-off we're examining today is whether Sana should prioritize expanding its AI-powered medical diagnosis tool to more specialties or focus on improving accuracy for existing covered conditions. This decision is crucial for Sana's growth strategy and product development roadmap. I'll analyze this trade-off by considering various factors including business impact, user needs, technical feasibility, and long-term strategic implications.

Step 1

Clarifying Questions (3 minutes)

• Context: I'm assuming Sana's AI diagnosis tool is already established in certain medical specialties. Could you confirm which specialties are currently covered and how long the tool has been in use?

Why it matters: Helps understand the product's maturity and current market position Expected answer: Tool covers 3-5 major specialties, in use for 1-2 years Impact on approach: Longer use suggests focusing on accuracy, newer tool might lean towards expansion

• Business Context: Based on Sana's current revenue model, I'm thinking the expansion vs. accuracy improvement might impact different revenue streams. Can you share how the tool is monetized and which approach aligns more closely with immediate revenue goals?

Why it matters: Aligns solution with business objectives and revenue strategy Expected answer: Subscription model for healthcare providers, expansion could increase subscriptions Impact on approach: If expansion directly ties to revenue, it might take priority

• User Impact: Considering the tool's users, I'm curious about the feedback we've received. Have users expressed a stronger desire for broader specialty coverage or improved accuracy in existing areas?

Why it matters: Ensures solution addresses actual user needs and pain points Expected answer: Mixed feedback, with some users requesting new specialties and others emphasizing accuracy Impact on approach: Would help prioritize based on most pressing user needs

• Technical Feasibility: Looking at our AI capabilities, I'm wondering about the relative complexity of expanding to new specialties versus improving accuracy. Could you provide insight into the technical challenges for each approach?

Why it matters: Assesses feasibility and resource requirements for each option Expected answer: Expansion requires new data sets and model training, accuracy improvement involves refining existing models Impact on approach: Technical complexity could influence timeline and resource allocation

• Resource Allocation: Considering our current team structure, I'm thinking about how our resources are allocated. Can you share if we have dedicated teams for expansion and accuracy improvement, or if we'd need to reallocate resources for either approach?

Why it matters: Determines if we have the right team structure to execute either option Expected answer: Current team focuses on maintaining existing specialties, would need reallocation for either option Impact on approach: Might influence decision based on team expertise and capacity