How can ShiftKey improve its shift scheduling algorithm to better match healthcare professionals with facilities?

Introduction

To improve ShiftKey's shift scheduling algorithm for better matching healthcare professionals with facilities, we need to dive deep into the current system, user needs, and market dynamics. I'll approach this challenge by analyzing key stakeholders, identifying pain points, generating innovative solutions, and proposing a strategic implementation plan. Let's begin by clarifying some crucial aspects of the problem.

Step 1

Clarifying Questions (5 mins)

• Looking at the healthcare staffing landscape, I'm thinking ShiftKey might be facing increasing competition and pressure to optimize its matching efficiency. Could you share insights on our current market position and how it's influencing this improvement initiative?

Why it matters: Determines if we focus on differentiation or operational excellence Expected answer: Strong market position but facing new entrants with advanced AI capabilities Impact on approach: Would emphasize AI and machine learning enhancements to maintain leadership

• Considering the complexity of healthcare scheduling, I'm curious about the current algorithm's performance metrics. What are the key performance indicators (KPIs) we're currently tracking, and how are they trending?

Why it matters: Identifies specific areas for improvement and sets baseline for measuring success Expected answer: Key metrics include fill rate, time-to-fill, and provider satisfaction, with recent plateaus in performance Impact on approach: Would prioritize solutions targeting the most critical underperforming metrics

• Given the critical nature of healthcare staffing, I'm wondering about the balance between automation and human oversight in our current process. Can you describe the level of human intervention required in the current matching process?

Why it matters: Determines the scope for further automation vs. enhancing human decision support Expected answer: Significant human intervention required for complex cases and dispute resolution Impact on approach: Would focus on developing a hybrid system that leverages AI while maintaining necessary human oversight

• Considering the diverse needs of healthcare facilities, I'm thinking about the granularity of our current matching criteria. How detailed are the facility requirements and healthcare professional profiles in our current system?

Why it matters: Identifies potential areas for more nuanced matching and personalization Expected answer: Basic matching on role, qualifications, and availability, with limited consideration of soft skills or cultural fit Impact on approach: Would explore incorporating more sophisticated matching criteria and preference learning