Roles & Contributions 

Problem Identification

Why we chose health: Our team ultimately ended up choosing the health theme due to a shared interest in the intersection of CS and healthcare applications, specifically regarding the efficiency of healthcare delivery. Additionally, many teammates have relatives and are closely connected to individuals in the space. Given the high-stress environment of clinical settings, we identified a significant opportunity through previous research and pain point discoveries and saw that in interviews, many individuals said that UX principles reduce the cognitive load on healthcare providers.

Brainstorming: Our team conducted a pain point exploration time during class where we explored various sectors (education and agriculture primarily). We pivoted to nursing workflows after researching the main problems of such the hectic environments that is hospital wards, leading to us identifying issues related to alarm fatigue. 

Findings: According to the American Association of Critical-Care Nurses, up to 72% to 99% of clinical alarms are false or clinically insignificant, and can lead to "alarm fatigue" where nurses become desensitized to alerts. We specifically looked at how noise pollution and "information silos" at nursing stations contribute to burnout. We identified that while patient data is abundant, its delivery is often intrusive because of loud alarms or buried deep within an EMR. The current systems rely on auditory interrupt signals and when every minor fluctuation triggers the same/similar beep, critical life-threatening events can be missed. 

It became clear that there is a lack of ambient and non-intrusive data visualization. Existing Electronic Medical Record Systems are that “pull” information where nurses must go to a computer to check status and our system transitions this to a passive “push” ambient display already displaying patients immediate statistics.

Client

The client is a hospital organization where a relative, who is a medical resident, is employed. The client group was selected because a few of our team members have relatives currently in residency who have firsthand experience using pager systems. This connection provided valuable insight into the needs and challenges associated with pager use as well as providing timely feedback. 

We came up with this choice because of how willing and eager they were to respond and react to our idea. They immediately gave us information we used in our research (See Phase 1).

Concept Statement

The Nurse Tracking Wall (NTW) is an ambient, peripheral display system designed for hospital ward nursing stations to lessen the incredibly high cognitive overload caused by chronic alarm fatigue from pagers. By translating dozens of complex patient vitals into intuitive, "at-a-glance" visual cues, NTW allows for a broad-level situational awareness for nurses without the cluttered mess of traditional monitors. The system replaces standard, unreliable communication by integrating with a redesigned haptic pager. This creates a direct link where nurses silently acknowledge alerts and respond through a haptic-feedback loop that instantly updates the wall’s patient statistics. Unlike standard EMR dashboards that require active searching, NTW serves as a supplement and a non-distracting source of important info that reduces response times to critical events while minimizing the desensitization caused by false-positive auditory alarms. Ultimately, the NTW empowers nursing staff to prioritize patient care through streamlined, non-intrusive data visualization and communication. 

Technical Summary

Our project is centered around the use of a room-indexed “status wall” mounted at the nurses’ station paired with a nurse pager/app, designed to reduce alarm fatigue by shifting most alerts from constant audible beeping to a clear, shared, visual picture of unit risk and responsibility. 

Each patient room is represented by a single block on the wall. That block does not simply mirror raw vital signs; instead, it continuously combines bedside monitor data (heart rate, blood pressure, oxygen saturation, respiratory rate, ventilator data), device/technical signals (lead off, probe poor contact), and selected clinical context (ordered target ranges, diagnosis-relevant baselines, “expected” or “abnormal” flags) into a patient-specific risk state that answers: “Does someone need to do something now?” 

The block’s main color represents urgency (normal → watch → needs attention → urgent), motion represents worsening trend (not the raw value), and a system icon shows the likely problem type (respiratory, hemodynamic, neuro, technical). The system also shows confidence: if data quality is poor or unreliable, the block visibly indicates low reliability so nurses can distinguish “real deterioration” from “sensor noise.” Nurses can also tap a block on the wall or on their pager/app to expand details (recent trends, contributing signals, last acknowledged event, and what the system thinks is driving risk), then take actions if needed (e.g. stuff like acknowledge, silence, or mark as “normal” or “to monitor more closely”). 

Every action updates the shared wall state so the whole team sees who owns it and what is happening. Most importantly, the users can define exactly when sound is used and what sound is used: audible alarms can be reserved for true “must-not-miss” events (for example, lethal arrhythmias, sustained severe hypoxia or hypotension, ventilator disconnection, or unacknowledged urgent states), while routine threshold crossings and transient events can be routed to the wall/pager as visual + haptic notifications (instead of just sound) with built-in persistence rules (alerts that last long enough or worsen is prioritized and creates sound). This makes alarms fewer, more meaningful, and easier to assess: nurses get a reliable “top of the list” view of what matters most across the unit, can quickly see whether an abnormal value is expected or transient, and can coordinate response without duplicated work or silent alarm disabling.

Key Features

• Visualization and actionability over raw data: the wall shows a per-room risk state and “why,” not just numbers crossing lines.

• Confidence/quality built in: alerts are visibly flagged and given a confidence value so they don’t look like patient crises.

• Explicit escalation rules: urgent states escalate if not acknowledged/assigned; resolved states decay back automatically.

• Alarm replacement: most nuisance alarms become silent/visual; audible alarms are limited to defined high-risk cases and device failures.

• Drill-down on demand: a single tap reveals trends, targets, and contributing signals without cluttering the main wall view.

• Customizable: alarms, thresholds, and visuals can be modified according to expert judgement