← Maven Clinic Interview Insights
This is a big one and I spent probably too long on the intake form side before they nudged me toward the matching engine.
Start by clarifying functional and non-functional requirements, then design the data model and core APIs for each stage of the patient-therapist journey. Focus on the matching algorithm and how you'll handle real-time interactions like messaging and booking, while ensuring scalability, privacy, and reliability.
Pro tip: Emphasize the importance of data privacy (HIPAA compliance) and how you would design the system to be extensible for future features like video sessions or insurance integration. Also, discuss trade-offs in your design choices, such as using a simple ranking algorithm vs. a machine learning model.
Ask questions to understand the scope: expected user volume, key features (intake, matching, booking, messaging, reviews), and non-functional requirements like latency, availability, and compliance (HIPAA).
Define core entities: Patient, Therapist, IntakeForm, Match, Appointment, Message, Review. Consider relationships, indexes, and how to store sensitive data securely.
Outline RESTful or GraphQL APIs for each flow: intake submission, profile management, matching (with ranking), booking, messaging (real-time via WebSockets), and reviews. Discuss service boundaries and potential microservices.
Explain how to match patients to therapists based on criteria (specialty, availability, location, preferences). Discuss ranking factors (e.g., therapist rating, experience, patient feedback) and how to compute scores efficiently.
Address scaling (caching, sharding, read replicas), handling failures (retries, circuit breakers), and security (encryption, access control, audit logs). Mention monitoring and analytics.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I knew this was coming for a healthcare company but still blanked for a second on the specifics.
Start by acknowledging that HIPAA compliance is a shared responsibility and must be baked into every layer of the architecture, not bolted on. Then walk through the key areas: data encryption, access controls, audit logging, and data retention, explaining how each addresses PHI protection. Finally, discuss trade-offs like performance vs. security and how you would validate compliance through testing and monitoring.
Pro tip: Emphasize that audit logs themselves contain PHI and must be protected with the same rigor—encrypted, access-controlled, and immutable. Also, mention that you would involve legal/compliance early to ensure alignment with regulations beyond HIPAA, such as GDPR for Maven Clinic's global user base.
Map where PHI is collected, stored, processed, and transmitted across the system. This helps determine where controls are needed.
Enforce least privilege with RBAC, MFA, and encryption at rest and in transit. Ensure only authorized services and users can access PHI.
Log all access and modifications to PHI with who, what, when, and why. Store logs securely, immutably, and separately from application data.
Define retention policies per HIPAA and business needs, and implement secure deletion when data is no longer needed.
Continuously monitor for anomalies, conduct regular audits and penetration tests, and update controls as threats evolve.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements and constraints, such as expected concurrency and consistency needs. Then propose a layered solution: database-level locking (e.g., unique constraint or SELECT FOR UPDATE) as the primary defense, with application-level checks and idempotency to handle retries. Finally, discuss trade-offs and scalability considerations.
Pro tip: Mention that you would use a unique constraint on (therapist_id, start_time) as the ultimate safeguard, and handle the resulting exception gracefully to return a user-friendly message. This shows you prioritize data integrity and user experience.
Ask about expected load, consistency requirements (strong vs eventual), and whether the system is distributed. This ensures your solution aligns with the actual needs.
Propose using a unique constraint on (therapist_id, appointment_time) or SELECT FOR UPDATE to lock the row during booking. This prevents concurrent inserts/updates from creating duplicates.
Implement an optimistic check before attempting the booking, but rely on the database as the source of truth. Use transactions to ensure atomicity.
Design the booking endpoint to be idempotent using a client-generated request ID, so retries don't create duplicate bookings. Handle unique constraint violations by returning a clear error.
Discuss how the solution scales (e.g., database locks can be a bottleneck) and alternatives like distributed locks (Redis) or queue-based serialization, weighing complexity vs. consistency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This was my favorite part of the conversation.
Start by clarifying the product context—what 'matching' means at Maven (e.g., connecting patients to providers) and the constraints (latency, fairness, compliance). Then walk through a layered pipeline: hard filters first to ensure eligibility and safety, followed by a scoring model that blends specialty fit, historical outcomes, and availability, and finally a ranking step with tie-breakers and business rules. Emphasize how you'd validate and iterate using offline metrics and online A/B tests.
Pro tip: Show you understand that hard filters aren't just technical—they encode clinical, legal, and business rules (e.g., licensure, insurance). Mention that you'd make filters configurable and auditable, and that you'd monitor filter impact to avoid over-constraining the candidate pool.
Ask about the matching domain (e.g., patient-to-provider), key objectives (e.g., best clinical fit, availability, cost), and non-negotiable constraints (licensure, insurance, language). Establish latency and scale expectations.
Define binary eligibility criteria that must be met (e.g., provider licensed in patient's state, accepts insurance, has availability). Implement as a fast pre-filtering layer, possibly using a rules engine or database queries, and ensure it's auditable and configurable.
Identify signals like specialty match, past patient outcomes (e.g., satisfaction, adherence), provider experience with similar conditions, and proximity. Normalize and weight them based on business priorities, using a weighted sum or learned model.
Apply scoring to filtered candidates, then rank by score. Incorporate tie-breakers (e.g., earliest availability) and business rules (e.g., promote providers with low wait times). Consider diversity or fairness adjustments if needed.
Use offline evaluation (e.g., historical data, precision@k) and online A/B tests to measure impact on key metrics (e.g., match acceptance, patient outcomes). Monitor for filter over-constraint and adjust weights or add signals.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the consistency requirements and failure modes, then propose an event-driven architecture with a single source of truth for availability. Discuss trade-offs between strong and eventual consistency, and how to handle conflicts and stale data.
Pro tip: Emphasize idempotency and reconciliation: even with events, you need a periodic full sync to correct drift, and all updates should be idempotent to handle retries safely.
Ask about read/write patterns, latency tolerance, consistency needs (e.g., double-booking prevention), and scale. This shows you don't jump to solutions.
Propose that the booking system owns therapist availability, as it handles real-time bookings. The search index is a derived read model.
Use an event-driven approach: booking system emits availability change events (e.g., via Kafka) that update the search index. Discuss eventual consistency and fallback mechanisms.
Handle event loss, duplicates, and out-of-order updates with idempotent consumers and versioning. Implement a reconciliation job for periodic full sync.
Compare with synchronous dual-writes (fragile) or change data capture (CDC). Discuss latency vs. consistency and how to monitor and alert on drift.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.