Reducing stroke-to-treatment time through EEG utility implementation in an application for Emergency Medical Services
Jacobs Medical Center
3.7 mi
12 minutes
Scripps Green Hospital
4.5 mi
15 minutes
Sharp Memorial Hospital
6.7
24 minutes
Hillcrest Medical Center
7.7 mi
30 minutes
Scripps Memorial Hospital
8 minutes
2.3 mi
4
3
3
3
Kaiser Permanente
3.1 mi
11 minutes
2
2
Send Patient Information to Hospital
Last Well Time: 3:12 PM
Pre-existing Conditions:
High blood pressure, diabetes, and previously experienced a stroke.
Current Conditions:
Face Drooping, Aphasia, Hemiplegia, Abnormal Eye movement.
Patient: Margot Ross
Age: 73
Sex: Female
DOB: 01/26/1852
View Full Patient History
Scripps Memorial Hospital
2.3 mi
8 minutes
Level 4 Comprehensive Center
BANAMBULANCE
3:48 PM
Patient: Margot Ross
Hospital Options
Home
Strokes are the 4th leading cause of death in the United States, with 800,000 cases a year.
Our application, Banambulance, changes the game:
In order to understand the problem and our users, we interviewed 2 EMTs and a stroke survivor.
From our prior research, we knew TIME was a critical portion that needed to be saved to increase a stroke patient’s chances of survival + reduce disability.
But from talking to real EMTs, we had 3 main huge findings:
With these findings, we created our user personas and storyboards to begin formulating a way to relieve our user’s pain points.
EMTs go through a protocol called BEFAST
EMTs are not always confident based on this behavioral assessment
Identifies a stroke patient’s condition based on a behavioral assessment.
Balance, Eyes, Face, Arms, Speech, Time
Knowing the patient history and brain data helps the EMTs deduce whether the symptoms displayed are caused by a stroke or were pre-existing
Some patients can display stroke-like symptoms without actually having a stroke, OR don’t display stroke-like symptoms when they are actually having a stroke.
Solution
Research
In order to address these pain points, we created a solution that includes the features stated above, creating our first low fidelity wireframe designs of our application.
Lo Fi Designs
User Personas
Storyboards
After user testing with one of our EMTs, he gave input that drove our reiterations for our high fidelity screens and prototype.
Below are a couple of our reiterated High Fidelity screens.
Hi Fi Designs
Our goal was to solve the problem of time lost between stroke onset and receiving treatment. Creating a solution that was intuitive and clear for an EMT to understand in a fast-paced environment was critical for this process.
With Banambulance, this becomes more possible than ever.
What I Learned
Working alongside aspiring neuroscientists and UX designers made understanding the weight of stroke misdiagnosis greater.
It no longer seemed as a distant problem that I’ve only heard of, but a pain that I understood deep in my own heart.
I’ve learned how to have the deep understanding of our user’s pain point be the driving factor of all our design decisions. This relationship between the designer and the user allows design decisions to actually address the problem at hand.
Tying visual design with solution design is a skill I have not only attained, but one that I will continue to nurture onto the next.
Reflection
Problem
My Role
Team
Skills
Timeline
Content Strategist & UX Researcher
Content Strategist, UX Researcher
UI/UX Designer
Neuroscientist
Market & User research
UI/UX Design
User Testing
5 weeks
Aug 2025 - Sept 2025
kate hong
design
powered by doodles and daydreams <3
In such critical circumstances, up to 22% of strokes are missed and misdiagnosed by EMS in the field.
A lack of access to the patient’s prior medical history and objective diagnostic tools, resulting in higher chances of disability and lower likelihood of survival.
Marcus Hayes
San Diego, CA
EMT
Married
Male
32
LOCATION
OCCUPATION
STATUS
GENDER
AGE
“I want to save everyone”
PAIN POINTS
Difficult to understand EEG displays and results under fast, intense, and high-pressure environments
Having to juggle multiple devices and screens while managing patient care
GOALS
Save lives in time-critical situations
Feel confident in interpreting pre-hospital EEG results
Minimize errors by using clear and reliable data
Quickly share EEG data with hospital before arrival
MOTIVATIONS
Marcus is passionate about his job and loves helping others. He wants to make sure that every patient gets the aid they need after he hands them off to the hospital. Marcus wants to be in more control of his interpretation of EEG results.
BIOGRAPHY
Marcus has been a EMT for 9 years, working in both urban and rural areas. He relies heavily on quick diagnostic tools for stroke patients. While he has used portable EEG devices before, he finds their interface clustered and difficult to understand while under a lot of stress.
Aurora Williams
San Diego, CA
Charge Nurse
Not Married
Female
35
LOCATION
OCCUPATION
STATUS
GENDER
AGE
“Every minute counts”
PAIN POINTS
Lack of patient data before arrival, so doesn’t know if they have the proper facilities or treatment for patient; has to send away patients when they don’t
Has to prepare medical teams last-minute to receive patients
GOALS
Gather necessary patient data quickly, based on information received from EMT
Prepare a team at the hospital before arrival to provide proper medical attention on a timely manner --- every minute counts!
MOTIVATIONS
Aurora is passionate about people’s health and providing the best care for them since studying biology at UCSD in her undergraduate years. Since she’s not married, she dedicates most of her time caring for patients so they can have good recovery for their best life.
BIOGRAPHY
Aurora is a 35 year old charge nurse at a nearby hospital in San Diego. She has been working in the hospital for 11 years, and has had various experiences encountering stroke patients. She is usually very patient with people and likes to do her job as well as possible.
Gives EMTs more confidence in their calls and decisions because they have more data to back up their BEFAST
About 87% of these strokes are the ischemic type, which is a blood clot in the brain.
Every minute a stroke goes untreated, the brain loses 1.9 million neurons.
Ischemic strokes can be treated within the right time frame — meaning time is key to a patient’s survival.
Several interviews with EMTs,
As well as with a surviving stroke patient’s mother.
an EEG cap providing clear, real-time brain data
incorporation of BEFAST, EMS current stroke protocol
We bring in critical information that equips EMS to more accurately identify strokes on the spot.
With 87% of strokes being ischemic and treatable if caught early, our interface helps save time, save lives, and give patients a better chance at recovery.
patient history input with a mic
Okay, we have patient data. So then why EEG?
This can be a GAME-CHANGER to provide EMS with this information.
EEG stands for Electroencephalogram, which involves placing small, metal disks onto the scalp to read brain waves and assess brain function.
It has the ability to detect abnormalities caused by ischemic strokes.
John Doe
Patient:
Sun Aug 24 1:30 PM
nihss scale
eeg output data
pateint info?
rec hospitals
Last-Well Time:
12:05 PM
Pre-existing Behavior:
Current Behavior:
BPM BP
UCSD Scripps
Kaiser Permanente
John Muir Health
whsdfkjha
whsdfkjha
NIHSS Suggested Score
Patient Behavioral History
Recommended Hospital
34: Severe
0
42
EEG Output Graph
Normal
Stroke
Lo Fi Wireframes
Patient History
Last Well Time: 3:12 PM
Live Transcript:
“The patient always had a prior problem with dizziness because they have vertigo. They also always had a limp arm because of nerve damage due to a prior car accident years ago. When questioned, the patient...”
Name: Margot Ross
Age: 73
Sex: Female
BEFAST Check
What are the patient’s current conditions?
BANAMBULANCE
3:48 PM
Patient: Margot Ross
Existed
Balance
Eyes
Face
Arms
Speech
Normal
(0)
Slightly Severe
(3)
Severe
(4)
Very Severe (5)
Moderate (2)
Slight
(1)
View Full Patient History
Jacobs Medical Center
3.7 mi
12 minutes
Scripps Green Hospital
4.5 mi
15 minutes
Sharp Memorial Hospital
6.7
24 minutes
Hillcrest Medical Center
7.7 mi
30 minutes
Scripps Memorial Hospital
8 minutes
2.3 mi
4
3
3
3
Kaiser Permanente
3.1 mi
11 minutes
2
2
Send Patient Information to Hospital
Last Well Time: 3:12 PM
Pre-existing Conditions:
High blood pressure, diabetes, and previously experienced a stroke.
Current Conditions:
Face Drooping, Aphasia, Hemiplegia, Abnormal Eye movement.
Patient: Margot Ross
Age: 73
Sex: Female
DOB: 01/26/1852
View Full Patient History
Scripps Memorial Hospital
2.3 mi
8 minutes
Level 4 Comprehensive Center
BANAMBULANCE
3:48 PM
Patient: Margot Ross
Hospital Options
Home
You can access the full experience on our prototype here!
Table of Contents
Problem
Solution
Research
Lo-Fi Design
Hi Fi Design
Reflection