How should Komodo Health prioritize expanding its Healthcare Map coverage versus improving data accuracy for existing entries?

Introduction

The trade-off between expanding Komodo Health's Healthcare Map coverage and improving data accuracy for existing entries is a critical decision that will shape our product strategy and impact our ability to serve healthcare stakeholders effectively. This scenario involves balancing the breadth of our data against its depth and reliability. I'll analyze this trade-off by examining its implications for our business, users, and technical infrastructure, ultimately providing a recommendation based on a data-driven approach.

Step 1

Clarifying Questions (3 minutes)

• Context: I'm thinking about the current state of our Healthcare Map. Could you provide more details on our current coverage percentage and the average accuracy rate of our existing data?

Why it matters: Helps establish a baseline for improvement Expected answer: 70% coverage, 85% accuracy Impact: Higher coverage might prioritize expansion, while lower accuracy would emphasize improvement

• Business Context: Based on our revenue model, I assume data licensing is a key revenue stream. How does data accuracy versus coverage impact our pricing and customer retention?

Why it matters: Aligns decision with revenue implications Expected answer: Both factors influence pricing, but accuracy is critical for retention Impact: Might lean towards improving accuracy if it significantly affects customer retention

• User Impact: Considering our user segments, how do different stakeholders (e.g., pharma, researchers, providers) prioritize coverage versus accuracy?

Why it matters: Ensures solution addresses key user needs Expected answer: Varied preferences, with researchers valuing coverage and providers prioritizing accuracy Impact: Might suggest a segmented approach to the trade-off

• Technical Feasibility: Regarding our data infrastructure, what are the relative complexities of expanding coverage versus improving accuracy?

Why it matters: Assesses technical constraints and resource allocation Expected answer: Expansion is more straightforward, accuracy improvement requires advanced algorithms Impact: Could influence timeline and resource allocation in favor of the less complex option

• Timeline and Resources: Given our current team capacity and budget, what's the realistic timeframe for significant improvements in either area?

Why it matters: Helps set realistic goals and expectations Expected answer: 6-12 months for meaningful improvements in either area Impact: Shorter timelines might favor focusing on one aspect over trying to balance both