Patients submit symptoms through NovaCare before they call 811 or visit the hospital. By the time a healthcare practitioner connects with them, the intake is already done — the conversation is about follow-up, not data collection.
This project began as a Challenge Nova Scotia 2025 submission (Team 59, November 14) on the theme of Human–AI Collaboration in Healthcare. We explored four AI-in-healthcare ideas, then scoped down to one actionable concept and built it as a full class project in under a month.
Our original submission explored four Human–AI collaboration ideas for Nova Scotia healthcare: robotic surgery, telemedicine, predictive care, and early disease detection — positioning the province as a leader in modern care delivery.
We narrowed to a single concept: a pre-visit symptom intake tool that sends structured patient data to healthcare practitioners — so calls and visits become about follow-up, not repetitive intake.
Six NSCC IT Campus students — spanning Business Intelligence & Analytics and Information Technology — brought together skills in BA, BI, development, and UX design.
With no dedicated web developer on the team, we used Lovable.dev to prototype the app rapidly. The PRD, decision trees, and use cases drove the build — Human–AI collaboration in practice.
Nova Scotia's healthcare system wastes time at every point of contact. When a patient calls 811, the first 10–15 minutes are spent collecting basic information — name, age, symptoms, duration, severity. When that same patient walks into an ER, the intake process starts over from scratch. The practitioner has no advance context. Every interaction begins at zero.
Meanwhile, the system is already strained. ER wait times average 3.5 hours and exceed 10 hours in congested areas. The 811 tele-nurse service reports callback delays of over 4 hours. Over 87,000 Nova Scotians (8.2%) sit on a waitlist for a family doctor with no primary care pathway at all. Non-urgent patients fill emergency departments alongside urgent cases because there's no mechanism to sort before arrival.
The core inefficiency is simple: symptom intake happens live, in real time, consuming practitioner minutes that should be spent on clinical follow-up and care decisions. NovaCare addresses this by moving symptom collection upstream — patients submit their information through the app before the call or visit, so when a practitioner connects, the conversation starts at follow-up, not "tell me what's wrong."
Patients describe the same symptoms to 811, then again at triage, then again to the doctor. Every handoff restarts at zero.
Nurses and doctors spend the first minutes of every interaction on data collection instead of clinical assessment and care.
Hospitals have no advance notice of who's coming or what they need. Every patient is a surprise — making resource planning impossible.
NovaCare collects, structures, and forwards patient symptom data to healthcare practitioners — turning every 811 call and hospital visit from a cold start into an informed follow-up.
Patients enter demographics, temperature, symptoms, severity, and duration through a guided flow. AI-assisted follow-up questions refine the data — so the practitioner receives a complete picture, not a vague complaint.
Structured symptom data is sent to the receiving end — whether that's an 811 tele-nurse, a clinic, or a hospital triage desk. The call or visit starts at follow-up, not intake.
Find nearby hospitals with estimated wait times and pharmacies with hours and contact info. Geolocation-based search with map view and clickable phone numbers.
Critical symptoms — chest pain, severe bleeding, loss of consciousness — immediately bypass the intake flow and display emergency guidance with a direct 911 dial link. Safety first, always.
Three stages — patient, AI, practitioner. Each does what they do best.
Open app → consent → enter demographics, temperature, and symptoms. AI asks 5–7 follow-up questions to refine the picture.
AI parses input, detects red flags, structures data into a clinical-ready summary, and routes it to the right care channel.
811 nurse or hospital doctor receives the structured summary. The conversation starts with "I see you're experiencing…" — not "tell me what's wrong."
Requirements scoped and prioritized to drive the initial proof of concept, with clear boundaries on what's in and out for V1.
A full BA lifecycle — from charter and stakeholder analysis through to a working prototype, gap analysis, and solution evaluation.
Goals, scope boundaries, data sources, and out-of-scope items. Web/app-based AI tool for pre-visit symptom intake — no direct diagnosis.
Seven stakeholders mapped with power/interest levels. RACI matrix defines roles across a fictional implementation organization.
Requirements scoped into Must/Should/Could/Won't with pivot table for readability. Drove the proof of concept priorities.
Key partnerships (NS Health, ER departments, 811), value propositions, customer segments, revenue streams, and cost structures mapped.
End-to-end flow from app launch to data delivery to practitioners. Branching paths by symptom severity with red-flag emergency routing.
Fully described interactions: Enter Symptoms, App Launch, Hospital Information, Pharmacy Information, and more — with preconditions, flows, and exceptions.
17-section PRD covering personas, functional requirements, technical architecture, compliance (PIPEDA, PHIA), risk matrix, and phased roadmap.
Working web app built on Lovable.dev with React frontend and Supabase backend. Symptom intake flows, AI follow-up logic, and facility locators for Halifax/HRM.
Identified missing features across Pharmacies tab, Hospitals tab, and Pre-Screen flow — including broken directions, inaccurate wait times, and missing inputs.
Assessed alignment with original charter. Most backend resources, APIs, and practitioner-side integration not yet implemented — but highly functional for one month's work.
We documented what works, what doesn't, and where to take it next.