Industry

Healthcare

Employer

Alinea Health

Timeline

1 month

Automated WhatsApp Follow-up Bot for External Care Journey Recovery

WhatsApp Bot, Automation, SQL

How I leveraged WhatsApp as the highest-engagement channel to automate post-consultation follow-up, improving patient reconversion while uncovering platform limitations.

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Outcome

72%

response (target: 60%)

62%

return-to-care (target: 80%)

2

critical bottlenecks uncovered (WhatsApp proactive-msg limits + member resistance)

Context / Problem

Alinea is a B2B2C health-tech in PMF validation, backed by Founders Fund and General Catalyst. Members attended consultations and exams with external specialists, but there was no systematic capture of prescriptions, results, or reconversion back into Alinea’s preventive care journey. This blocked measurement of four critical metrics: claims cost reduction, reconversion into internal consultations, capture of exam results, centralization of the external-care journey.

My Role

Hybrid Product Manager / Product Designer working with 2 developers. Connected the CPO founder’s strategy to technical execution, designed the bot’s conversational flows, and orchestrated 3 clinical areas (physicians, nurses, concierge) across member touch points. Analyzed engagement data (Metabase/Mixpanel) to validate the channel and define success criteria.

Discovery

WhatsApp showed the highest engagement rates vs. the mobile app (Metabase/Mixpanel), validating it as the strongest channel for automated follow-up.

Solution

Automated WhatsApp flow with 3 paths after attendance verification: 1. Attended consultation → bot checks for new prescriptions → escalates to nurse to capture history → books return consultation 2. Attended exam → after 15 days bot requests result upload in the app 3. Did not attend → bot offers rescheduling via concierge

Chatbot messages confirming whether the user completed their consultation. If yes, the nurse is triggered for a direct follow-up. Includes the full chatbot flow with integrations across clinical teams and outcomes based on user responses.

Next Step

Prioritized 3 hypotheses to scale conversion: 1. Automated scheduling after attendance confirmation (target: 70% returns) 2. Internal-consultation suggestion before external scheduling to reduce unnecessary procedures (target: 50% conversion) 3. Capture reason for no-show to enrich provider-recommendation engine