Unicare — Colin LeBlanc

A system failing from every angle.

My mother is a registered nurse who burned out mid-pandemic and retired early. She's one of the hardest people I know. That raised questions I couldn't ignore about what was breaking down behind the scenes.

I spent the next year finding out. Thirty-two scholarly articles. Twenty-six public interviews. Year-long conversations with two retired nurses holding eighty combined years of experience. What I found: 94% of nurses reporting burnout symptoms, ER wait times surging 54% year-over-year, patients waiting 33 hours for a bed in Ontario, and no national system anywhere for hospitals to share what they'd learned.

One ecosystem, three products

My starting thesis was that the experience for every party in Canadian healthcare was unideal, and that technology could relieve all of it if designed as one system. Instead of a single tool for a single user, I built an interconnected platform: a patient portal, a bedside decision-support app, and a management dashboard, all pulling from the same data backbone. Improvement in one creates relief across all three.

Three products connect across patients, healthcare workers, and hospital management.

A co-pilot for healthcare workers.

The research pointed consistently to team-based care: distribute tasks by skill, not hierarchy. I built the bedside app around a daily huddle, workers assign roles at shift start, then get AI-powered guidance throughout. Every recommendation surfaces its reasoning so clinical autonomy stays intact.

The surface had to do two things at once: empower team-based care, and serve as a comprehensive companion tied to medical device data. That's why the home screen behaves like a mini dashboard with pressing alerts, your role for the shift, fast patient data access, and team status. The AI layer functions as an on-call advisor when a worker isn't sure what to do next.

Manual verification was a pragmatic choice for v1. Automated tracking would speed the crew further if it worked reliably, and it's the natural next addition once the trust and technical complexity are addressed.

Bedside decision-support app — home screen

Home screen surfaces alerts, role assignments, patient load, and team status at a glance.

Give patients the context to stop using the ER as a first resort.

Patients weren't misusing the ER out of laziness, they had no other way to understand what was happening with their health. Two decisions shaped the patient surface. First, consolidate the fragmented record. Canadian patient health data lives scattered across providers, devices, and portals, with no single source of truth, so the portal gives patients their records, connected device data, and appointment context in one place. Second, add self-triage. Research on Canadian ER wait times pointed to a pattern of patients using emergency departments as a one-stop-shop out of ignorance or necessity, and nurses interviewed echoed it. The self-triage flow walks patients from symptoms through targeted questions to a recommended care pathway based on their records and severity.

Patient portal — self-triage flow from symptoms to recommended care pathway

Self-triage walks patients from symptoms to a recommended care pathway based off of their health records, symptoms, and severity.

Turn trapped hospital data into shared intelligence.

The most striking finding from the research: when a hospital solves a hard problem, that knowledge stays local. Peer-reviewed research on Canadian Learning Health Systems puts it plainly: "LHS initiatives in Canada remain siloed and lack harmonized leadership, knowledge exchange, and capacity building." The hospitals making the biggest operational gains end up burying their own insights.

The admin dashboard does two things in response. It gives managers real-time visibility into team efficiency, doctor time on task, and patient mix. And it enables a two-tier data exchange built on patient de-identification: hospitals can share operational data freely with other hospitals to fill the lesson-learning gap, and sell the same data to research organizations as a sustainability lever for the hospitals doing the work.

De-identification sits in contested regulatory territory: re-identification risk, consent for secondary use, and provincial privacy law all complicate implementation. The design treats those as technology problems to solve, not design problems that should kill the model.

Management dashboard — nurse efficiency, doctor time on task, and data brokerage

Management dashboard showing nurse efficiency, doctor time on task, patient types, and de-identified data transaction volume.

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Results

ACIDO Rocket Finalist. Selected from hundreds of Industrial Design students in Ontario, nominated by thesis professor Alex Manu.

Validated with five stakeholder groups. Three prototyped products tested through card sorting, empathy mapping, value proposition canvases, and subject matter expert review.

What I'd do differently

Scope it like a startup. A three-surface ecosystem makes sense as academic exploration but is unrealistic for an early-stage company. I'd cut scope to one surface at launch and stage the rest as expansion phases tied to traction milestones.

Anchor on the hospital surface first. The patient surface is easier to design and feels closer to the user, but it sits at the top of the funnel and doesn't address the systemic issues driving ER misuse. The hospital-facing layer is the wedge: harder to sell, harder to design, but if it lands, it changes how care gets routed at the source. The right startup move is usually the counterintuitive one.

Metrics I'd track post-launch

Reduction in ER misuse rates. Tracked before and after patient portal adoption. The primary outcome for the consumer-facing self-triage.

Revenue from de-identified data brokerage. A sustainability signal for hospital-side adoption. If data exchange generates real revenue, hospitals gain a business reason to keep the system running.