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I built Sova Clinical for the Lovable challenge because clinical research sites still spend too much time turning early participant interest into actual screening appointments. The problem is not just scheduling. Coordinators often have to collect sensitive health information, review basic eligibility questions, go back and forth with participants, and then manage the booking manually. I wanted to reduce that friction without making the coordinator the first person who needs to see a participant’s health details.
Sova Clinical is a privacy-first pre-screening and scheduling system for clinical research sites. My approach was to separate the qualification workflow from the coordinator workflow. Instead of sending every raw answer directly to staff, Sova evaluates the study’s configured criteria first and gives the coordinator the operational outcome they actually need.
For the demonstration, Northstar Clinical Research is recruiting for a metabolic study. A potential participant completes the study's configured eligibility questions privately. Sova evaluates those criteria deterministically and returns PASS, FAIL or REVIEW, rather than guessing when information is unresolved.
That deterministic approach is intentional. I did not want an opaque model deciding whether someone should progress through a clinical screening workflow. If Sova can safely evaluate the configured criteria, it returns a result. If it cannot, it returns REVIEW and leaves the decision to a research professional.
A PASS unlocks persisted screening availability. The participant can select a slot, provide only the contact information required for the appointment, acknowledge that the result is not final clinical-trial eligibility, and confirm the appointment without coordinator intervention.
After booking, the participant receives a private access link that lets them return without creating an account, reschedule their appointment, or cancel it. Capacity updates automatically and safely as appointments move or are released.
The privacy-first adaptation is that the Northstar coordinator console receives the qualification outcome, booking information, capacity, and follow-through state, while the participant's individual pre-screen health responses remain withheld from the coordinator workflow. Northstar can act on the result without needing unnecessary access to the underlying answers.
The result is a workflow in which a participant can move from initial inquiry to a confirmed screening appointment with zero coordinator actions before confirmation.
Try the live demo at sova-clinical.lovable.app.
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As a psychologist and technical designer, I've always been interested in how technology can simplify complex workflows.
Managing a clinical practice involves much more than conducting therapy sessions. There are patient records, clinical notes, assessments, follow-ups, appointments, and administrative tasks. Each one takes time and attention that could otherwise be dedicated to patients.
Instead of adapting my workflow to another generic management platform, I decided to build something around the way I actually work.
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I wanted a clean interface where I could quickly access patient information, document sessions, identify clinical alerts, manage appointments, and work with psychological assessment tools.
Every design decision focused on reducing friction, improving information hierarchy, and making everyday tasks easier.
Bringing Local AI into the Workflow
One of my priorities was exploring how AI could support clinical work without making everything dependent on external cloud services.
By incorporating local AI capabilities, I could experiment with intelligent assistance while keeping privacy and control over sensitive information at the center of the design.
The goal was never to replace clinical judgment. It was to create a tool that helps me organize information, reduce repetitive work, and dedicate more attention to the human side of my profession.
Building Something I Actually Use
What started as a personal experiment became a practical tool for my clinical workflow.
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This project also changed how I think about building software.
You don't always need to start with a revolutionary idea. Sometimes, the most meaningful products begin with a problem you face every day.
Quinde Clínica is an ongoing personal project, and perhaps the most valuable thing I've learned is that understanding a problem deeply can be just as important as knowing how to code its solution.
