In today's busy emergency units, having clear, concise data is essential. Our dashboard brings together patient demographics, current readings and trend analyses in one unified view. It was developed with two core goals in mind: to reduce cognitive load for staff, and to ensure that no warning sign goes unnoticed.
As part of our project, we've just launched the first working iteration of our dashboard—built in Vue.js—to help healthcare professionals make faster, data-driven decisions. Designed for clarity and ease of use, this interface displays each patient's vital information at a glance, and highlights critical changes the moment they occur.
A Holistic View of Patient Data
When you first open the dashboard, you'll see a overview listing of every patient currently being monitored. For each entry, we display:
- Name, age and diagnosis, so you never lose context
- Latest heart-rate (FC), oxygen saturation (SpO₂) and body temperature,
Clicking on a patient brings up their full profile in the main panel, where you'll find not only the most recent readings but also chart showing how these values have evolved over time. Having that historical perspective helps clinicians detect subtle deteriorations or improvements that might otherwise slip through the cracks.
Designing for Intuitive Interaction
From the beginning, our emphasis has been on simplicity. Rather than overwhelming users with every detail, we guide their focus to exactly what matters:
- Real-Time Alerts: If a patient's SpO₂ dips below a critical level, or their heart rate/body temperature goes beyond a safe range, an alert will be emited. This ensures that urgent cases stay front and center.
- Trend Graphs: We use lightweight line charts to visualize the last measurements of Heart Rate, SpO₂ and body temperature. Hovering over any point reveals the exact timestamp and value—ideal for quick, precise assessments.
Under the Hood: Technology Stack
To deliver a seamless, responsive experience, our team selected technologies that balance performance with developer agility:
FastAPI Backend
Our server runs on FastAPI, which lets us process many requests concurrently without slowing down. It also generates API documentation automatically, and we've enabled cross-origin requests so our Vue.js front end can talk to it seamlessly (with the option to lock down allowed domains later).
MongoDB Time-Series Storage
With Wi-Fi and Bluetooth support, the Raspberry Pi can connect directly to multiple wearable devices. This makes it well-suited for reading data in parallel from several patient wristbands and serving as a local communication hub.
Instant, Auto-Updating Dashboard
A continuously running update loop checks for new measurements and pushes them straight to the browser. As a result, charts and alerts on the dashboard refresh themselves without any manual page reloads.
Easy Access to Full History
We also provide an endpoint that returns every reading for a given device in chronological order. This makes it simple to pull the full data set for deeper analysis or to generate external reports.
By combining FastAPI's speed, MongoDB's time-series efficiency and a constant data stream, our dashboard always reflects the true, up-to-the-moment condition of each patient.
What's next?
With the core dashboard in place, our immediate priority is to validate its real-world utility:
- Clinical Feedback Session: We'll demonstrate the dashboard to doctors and nurses at Hospital Santa Maria. Their insights on layout, alert thresholds, and workflow integration will be essential for fine-tuning the user experience.
- In-Hospital Pilot Testing: Following feedback, we plan a pilot deployment in the emergency waiting area. We'll measure system latency, user satisfaction and any technical hiccups under real operating conditions.
By combining an intuitive Vue.js interface with a robust Python-powered backend and MongoDB time-series storage, we've built a foundation that can scale from prototype to production. We look forward to sharing the next phase of our journey—and to helping emergency teams deliver faster, more informed care.
The complete source code for our Dashboard is available in our public repository: https://github.com/tomasmodesto/DEMS-APP . We encourage collaboration, feedback, and contributions from the community.