clini.cal
AI-powered healthcare scheduling concept, designed to make patient care proactive rather than reactive
TEAM
4 Engineers
TOOLS
FlutterFlow, Figma, Supabase, OpenAI API
DURATION
1 day
OVERVIEW_
What inspired this concept
Scheduling a doctor's appointment is broken, but not just because the process is clunky. It's because the entire system is reactive. You remember to book your annual physical after you get sick. You run out of a prescription before you think to call. By the time you're trying to get an appointment, your provider is already booked out for weeks.
The goal was to shift healthcare from reactive to proactive, and in doing so, take the anxiety out of scheduling altogether.
Note: This project was developed during SacHacks V, a 24-hour hackathon.
CHALLENGE_
What our team envisioned clini.cal to be
Let's imagine Clini.Cal had more than 24 hours for development…
Clini.Cal would learn your patterns over time - when you typically see your GP, when your dermatology visit usually falls, when a prescription renewal is coming up.
Instead of waiting for you to remember, it would surface a reminder at exactly the right moment: "Your doctor's calendar tends to fill up in March. Want to get ahead of it?"
On the day of care, live wait times at nearby clinics and pharmacies would make walk-ins something you could actually plan around rather than gamble on. And for anything in between, a built-in AI chatbot would offer general guidance on what kind of care you actually need before you even pick up the phone.
The throughline across all of it: less time managing your healthcare, more time actually receiving it.
PROCESS_
How we built Clini.Cal
The design process began with the creation of mid-fidelity wireframes in Figma. From the start, the priority was keeping the interface calm. Healthcare contexts are inherently stressful, and the UI needed to reflect that. Every decision defaulted to low cognitive load: bottom navigation, large tap targets, and plain language over medical jargon.
For development, we utilized FlutterFlow for the frontend, a database powered by Supabase for backend operations, and integrated the OpenAI API to implement an AI chatbot within the application.
Roadblocks we ran into
The primary challenge was implementing OpenAI's API and integrating the front-end (FlutterFlow) with the back-end.
On the free trial, FlutterFlow imposed significant limitations, and even after upgrading, remote collaboration remained a persistent issue as simultaneous editing required individual subscriptions across the team. Much of our time went into working around those constraints rather than refining the experience itself. Despite that, the goal for the chatbot remained consistent: clear affordances, transparent limitations, and a tone that never overstepped into replacing actual medical advice.
Back-end testing on local machines was hindered by technical incompatibilities and user permissions on Mac versus Windows. Furthermore, planned integration of a machine learning model for personalized appointment checklists was not feasible given the time constraints and the need to resolve the aforementioned technical hurdles.
OUTCOME_
What did I take away from this?
The biggest takeaway from Clini.Cal wasn't technical; it was that tool choice carries real consequences. FlutterFlow got us to a working frontend quickly, but its collaboration limitations slowed the team down at a critical point. That trade-off is something I would evaluate much earlier in the process next time.
