Designing an AI-Powered Remote Patient Monitoring System
Reducing time to treatment to save more lives
OVERVIEW
We designed a remote patient-monitoring screen for the London Air Ambulance and Royal Mary Hospital trauma bay team.
ROLE
OBJECTIVES
To minimize the time from arrival to treatment in emergency situations.
Our project, set within a strict four-month deadline for our master's program, demanded rapid progress. While the full implementation included a data input side, we preferred to focus on enhancing the user interface due to the time constraints and goals. This decision was strategic, aiming to prepare our design for future AI integration and ensure an on-time handover to the next team for further development. Additionally, designing for a new type of user presented a unique challenge that required special types of interaction and research.
Introduction
The London Air Ambulance (NHS) needed a tool to be able to facilitate communication between the helicopter paramedic team and the trauma bay team to save severely ill patients by monitoring them remotely en route to the hospital.
Paramedic-to-trauma bay communication as it stands now is mostly verbal, and there is no standardized or digitized way to stay in contact.
The London Air Ambulance (NHS) needed a tool to be able to facilitate communication between the helicopter paramedics team and the trauma bay team to save severely ill patients by monitoring them remotely en route to the hospital.
Problem
The first 60 minutes after a traumatic injury is the golden window that can determine a patient's life or death.
When a London Air Ambulance is dispatched to a trauma scene, the treatment starts immediately as soon as the injuries are identified and diagnosed. The helicopter emergency medical service (HEMS) team do their best to control and stabilize the patient on-scene and en-route until they arrive to the hospital trauma bay where they can recieve more complex treatments and surgeries.
However, the hospital trauma bay team often know very little information about the situation, events, and interventions that occur during and after the trauma. Upon arrival, a 30-second verbal handover from the paramedics to the trauma bay team takes place as they debrief them of the essential information. Soon after, the trauma bay team take over to re-examine and evaluate the patient once more, creating a redundant process.
research Insights
Voice-powered surveys gave us insights into many problems faced by medical personnel in the trauma bay.
We received insights and ideas that later influenced our design, such as the “REBOA Blood Control Timer,” a device installed in patients to control severe blood loss.
Modes, mediums, and styles of communication between the medical teams in emergency procedures were noted in a flow chart.
The Accordion Concept aimed to display historical and live trending patient data, but was too complex to be understood at a glance.
It proved to be the best decision when we tested it alongside the Accordion concept.
DATA VISUALIATION
We designed a panel with a live-stream visualization of patient data across a 5-second timescale and event-entry methods.
The patient’s ECG, oxygen saturation, and non-invasive blood pressure, are livestreamed through the ZOLL® X Series® monitor/defibrillator that is used by the HEMS team on-scene and en-route to the hospital. The monitor allows data entry in the form of interventions that update the screen’s intervention panel with respective timestamps.
With the assistance of the LAA dispatcher and senior hospital nurse, they can access the screen’s backend system to update manual data entries if needed, such as respiratory rate, C02 saturation, and the Glasgow coma scale. The visualization of the data graph was designed according to conventional patient monitoring systems
User TESTS
We used the 3-30-300 method of showcasing our interface and asking the testers questions to confirm whether they could read the interface easily.
User testing revealed how our product was percieved positively by medical personnel. Most claimed to understand what it does, and what it aims to solve. However, the AI prediction concept was under-developed, as many did not understand the need for it, let alone how it worked.
User perception
The role of human and clinical judgment in our AI feature was heavily criticized.
User testing revealed how our product was perceived positively by medical personnel. Most claimed to understand what it does, and what it aims to solve. However, the AI prediction concept was underdeveloped, as many did not understand the need for it, let alone how it worked.
