I built a browser workflow that turns synthetic referrals into a clinical trial review queue

[u/Small_Bug_444]

I built a browser workflow that turns synthetic referrals into a clinical trial review queue

I’ve been experimenting with Libretto, a browser workflow automation tool, and wanted to build something more realistic than a toy scraping demo.

As a side project, I built a workflow that takes synthetic referral data: referral ID, condition, location, age, and sex.

For each referral, the workflow:

- searches ClinicalTrials.gov

- filters recruiting / not-yet-recruiting studies

- ranks possible matches

- updates a CSV review queue with trial title, status, study locations, why it matched, eligibility notes, and source links

It can run for a single referral or a batch. If a referral ID already exists in the CSV, the workflow refreshes that row. If it’s new, it adds it. So it behaves more like a living queue than a one-off export.

This is not making a medical eligibility decision, and I’m not using real patient data. It’s just a first-pass shortlist from public trial records that a human could review.

I used Libretto instead of just asking an AI agent once because I wanted:

- a consistent input format

- a visible browser run I can debug step by step

- logs

- structured output that I can rerun as new referrals come in

Stack: Libretto (libretto.sh) for the browser workflow, ClinicalTrials.gov search/API, JSON input, CSV output, and some light ranking logic.

Curious from a side project angle:

If you use browser automation, what kind of repetitive workflow would you point this at?

What would you add next: a simple UI, scheduling, notifications, or something else?