$ cat projects/mayamd.mdx
MayaMD - AI-Assisted Healthcare Platform
Early-career full-stack work on patient onboarding, two laboratory integrations, MongoDB-backed report data, and printable clinical PDFs stored in Amazon S3.
React · Node.js · Express · MongoDB · Puppeteer · EJS · Amazon S3 · 2021 · live
Daffodil Software · Nov 2021 - May 2022
MayaMD was an AI-assisted telehealth and patient-engagement product supporting patient onboarding, medical-history collection, laboratory workflows, and printable clinical reports. It was my first full-time project at Daffodil, where I worked as a junior full-stack developer under senior review.
what I worked on
- Patient onboarding and medical history - built React screens and Node.js APIs for structured patient details, personal and family health history, and the downstream workflow that supplied the product's AI-assisted assessment.
- Two third-party laboratory integrations - submitted patient, sample, and requested-test data; polled for available results; parsed external responses; persisted the required report fields in MongoDB; and mapped them into an internal data model.
- Clinical PDF generation - independently learned Puppeteer and EJS to produce reports containing patient and doctor details, test values, reference ranges, and abnormal-result indicators. Generated files were stored in Amazon S3 for download and printing by healthcare workers.
what it taught me
This project built my foundation in full-stack delivery: reading a workflow carefully, defining request and response contracts, integrating systems outside the codebase, and learning a new tool quickly enough to ship a real feature.
ownership boundaries
I did not build the AI diagnostic model, design the database schemas, own deployments, or provide production support. My work covered assigned React and Node.js features, MongoDB queries, laboratory workflows, and report generation under senior review.