Design a product to help consumers find a doctor

Clarifying Assumptions

• We are building a consumer-facing product, not a B2B tool for insurers or hospital networks.
• The core problem is discovery and matching—not telehealth or EHR management.
• We target the US market where finding in-network, available doctors is a known friction point.

Product Motivation

Healthcare access starts with finding the right provider. Nearly 1 in 5 Americans delayed care in the last year because they couldn't find an available, affordable doctor. Existing solutions like Zocdoc are appointment-focused but fail on trust signals, specialty matching, and insurance complexity. For DoorDash—if we imagine this as a standalone or health-vertical product—the opportunity is to own the top-of-funnel for healthcare access, a trillion-dollar market with poor UX.

User Segments

• The Newly Insured: Recently joined a new employer plan and needs to establish care quickly. Doesn't know which doctors accept their insurance.
• The Specialist Seeker: Has a chronic condition or recent diagnosis and needs a specific specialist. Values credentials, patient reviews, and wait times.
• The Caregiver: Booking appointments for an elderly parent or child. Needs proxy booking, accessibility info, and location proximity.

Key Pain Points

• Insurance opacity: Users can't easily verify which doctors are in-network before booking, leading to unexpected bills.
• Availability blindness: Online directories list doctors who aren't accepting new patients—a massive waste of user time.
• Credential trust gap: Patients can't easily assess doctor quality beyond star ratings, which are sparse and unverified.
• Specialty mismatch: General search fails for nuanced needs (e.g., 'bilingual rheumatologist accepting Medicaid within 10 miles').

Solutions

1. Smart Insurance Matching (Priority: HIGH)

Integrate with insurance APIs (Availity, Change Healthcare) to surface only verified in-network doctors in real time. Show estimated out-of-pocket cost before booking. This directly kills the #1 pain point and differentiates from static directories.

2. Live Availability Layer (Priority: HIGH)

Partner with EHR systems (Epic, Athenahealth) to pull live appointment slots. Show 'Next Available' prominently. Users filter by 'within 48 hours' or 'within 2 weeks.' Reduce friction from discovery to booked appointment in under 3 minutes.

3. AI-Powered Need-to-Specialty Mapper (Priority: MEDIUM)

A conversational intake flow: user describes symptoms or condition in plain language. AI maps to appropriate specialty, surfaces top matches with rationale. Reduces wrong specialty bookings and educates users on their own care needs.

MVP Recommendation

Launch with Smart Insurance Matching + Live Availability as the core loop. This solves the two highest-friction problems and creates a defensible data moat through EHR integrations. The AI mapper can follow in v2 once we have sufficient user session data to train on real intake patterns.

Success Metrics

• Booking conversion rate: % of searches that result in a confirmed appointment — target 35%+ (vs. ~15% industry avg).
• In-network accuracy: % of bookings where insurance claim is actually covered — target 95%+.
• Time-to-booked: Median minutes from first search to confirmed appointment — target under 5 minutes.
• 7-day appointment kept rate: % of booked appointments that are attended — target 80%+.