what metrics would you use to evaluate doctolib's patient-doctor matching algorithm?

Introduction

Evaluating Doctolib's patient-doctor matching algorithm requires a comprehensive approach to product success metrics. This critical feature directly impacts user satisfaction, healthcare outcomes, and business performance. I'll follow a structured framework covering core metrics, supporting indicators, and risk factors while considering all key stakeholders.

Step 1

Product Context

Doctolib's patient-doctor matching algorithm is a core feature of their healthcare booking platform. It aims to connect patients with the most suitable healthcare providers based on various factors like specialty, location, availability, and patient preferences.

Key stakeholders include:

1. Patients: Seeking convenient, quality healthcare
2. Doctors: Looking to optimize their practice and patient load
3. Healthcare facilities: Aiming to improve resource utilization
4. Doctolib: Striving for platform growth and user satisfaction

User flow:

1. Patient inputs search criteria (specialty, location, etc.)
2. Algorithm processes request and presents matching doctors
3. Patient reviews options and books an appointment

This feature is crucial to Doctolib's strategy of streamlining healthcare access and improving patient outcomes. Competitors like ZocDoc and Practo offer similar services, but Doctolib's focus on the European market and integration with local healthcare systems sets it apart.

Product Lifecycle Stage: Growth - The algorithm is established but continually evolving to improve accuracy and user satisfaction.