Personal Health Dashboard

Context

Personal showcase app built as a private data product with serious auth, database, and validation boundaries.

Problem

Lab and wearable data becomes hard to inspect when values, units, freshness, ranges, and partial coverage all live in separate systems.

Role

Built the Next.js app, database schema, auth routes, upload/export paths, scoring logic, seed harnesses, and focused route/unit tests.

Constraints

• Health data needs authenticated access and export paths.
• Scores must avoid false precision when inputs are stale or partial.
• Local smoke testing needs a repeatable database fixture.

Approach

Modeled metrics, categories, lab reports, wearable days, custom ranges, providers, and export endpoints in Postgres through Drizzle, then wrapped the product flows with unit, route, DB, and Playwright smoke coverage.

Challenges

The scoring model had to communicate partial-data states instead of inventing certainty from incomplete coverage.

Impact

• Private demo evidence: authenticated product surface for lab data, wearable summaries, custom ranges, score states, and export paths.
• Repo evidence: auth/API route tests, migration tests, local Postgres smoke scripts, seeded browser smoke path, and export endpoints.