TeleTracking - Helping victims of disasters

Throughout man-made or natural disasters, hospitals face a surge of patients and non-patients alike, looking for medical care, shelter and protection. Seeking help at hospitals that are already overstrechted may cost the lives of injured victims. Citizens must therefore be directed to shelter locations capable of accommodating them.

Haven is an app that helps citizens to better prepare themselves to survive hurricanes and to relocate to shelters other than hospitals.

PNC project picture



8 months

5 person team

Master's Capstone Project

My roles

UX Researcher

UX Designer

Tech Lead


Affinity Diagrams


Design Sprints

User Testing

Conducting co-design activities


Paper + Pencil


HTML/CSS + JavaScript


Haven is an app designed to help special medical needs citizens and their caregivers to find the most appropriate shelter from an upcoming hurricane. Users will be able to:

  1. Register special medical needs citizens into a special medical needs shelter.
  2. Access a personalized preparation list.
  3. Access personalized evacuation plans that will be updated based on external conditions.
mid fidelity screens mid fidelity screens mid fidelity screens

Understanding the Problem Space

Project Kickoff

We met with TeleTracking to better understand the problem space. We asked them what their main goals for the project were, explained our design process and conducted a joint design activity with them: they were asked to create a TeleTracking persona, somebody that would impersonate the company's main values and goals.

kickoff picture

Initial Research Methods

In the first three months, the team focused on understanding the problem space, using different techniques:


To better understand how the general population deals with disasters.


To get a broader view of different kinds of disasters and how people are affected by them.


To get specific points of view from hospitals, disaster experts and disaster survivors.


To identify general problems faced by disaster survivors.

target model

Guerilla Research

By initially concentrating on ordinary citizens who had already been through a disaster, some of our key findings were:

target model

When facing a disaster people might panic and not follow the procedures, even if they know them.

target model

Supermarkets may be used as shelters since they could provide heat and electricity.

target model

Disaster preparedness and knowledge often comes from school, movies and the news.


We submitted a survey to MTurk to obtain additional information on areas we still had a gap on secondary research data. The survey was answered by 98 people that had experienced disasters.

We found out that hospitals are still a fairly popular shelter location.

target model

We wanted to understand the main reasons why people seek shelter at hospitals:

target model


We decided it was important to acquire additional detailed information on some particular areas. In order to obtain it, we conducted interviews with:

target model

Some insights were:

  1. Hospitals must have the capability of tracking down non patients within their facilities.
  2. If special need shelters are full, people tend to be sent to hospitals. Hospitals would then look after them, but they would not be considered as patients.
  3. Frequently, citizens don't take disasters seriously enough. Eventually, when they do realize they need shelter, hospitals might be the closest safe locations available.

Affinity Diagramming

In order to better understand all the information obtained from interviews, we set up an affinity diagram to cluster information.

target model

Generating Initial Ideas

After research, we generated and tested ideas by using the following methods:





Stakeholder model

In order to generate more ideas in a faster and more creative way, we first used our stakeholder model to identify pain points between the entities and the values exchanged by them.

low fidelity wireframe

Design Sprints

By generating ideas through design sprints, we were able to be creative and think out of the box. The methods used were:


Pitching viable ideas based on previous research.


Thinking about how we might solve previously identified problems.


Generating 8 different ideas in 8 minutes.


Collaborative sketching to generate solutions based on How Might We prompts.

Storyboarding & Speeddating

From all ideas formulated in the design sprint we selected the most promising ones and created 6 storyboards, each portraying a different solution. Selected ideas focused on citizen redirection (2x), hospital expansion, forcing citizen action, tracking non-patients, having non-patients at hospitals as volunteers.

storyboard 1
storyboard 2

In order to evaluate our ideas we had an onsite client meeting, where we showed them the storyboards and evaluated their reactions. We wanted to understand whether or not users would feel the need for using and adopting our proposed solutions.

The client reacted well to the ideas, but indicated that:

  1. The solution should be more citizen focused.
  2. There was no restriction on the kind of technology the final solution uses.
  3. The solution could be a service but it should have a product (such as an app) as a part of it.

Reverse assumptions and 20 questions

Taking into account client feedback, the team conducted another design sprint, using a methodology that would help us reframe the problem and look at it from a new perspective.

reverse assumptions

Design Idea

Paper prototype

The team discussed the ideas generated after the Reverse Assumption activity and created a low-fidelity prototype of our main idea: an app for citizens to obtain real-time information from shelters. We then created and tested screens with users by conducting think alouds.

low-fidelity prototype

Parallel Prototyping

Each team member separately created low-fidelity screens for the whole app flow. These screens were then compared and combined into a final medium-fidelity version.

low-fidelity prototype

User Testing

The team travelled to Florida to test our solution with caregivers, special medical needs citizens and general citizens who had been through hurricanes. We followed a protocol and had user's thinking-aloud to understand their mental models while using the app.

user testing

Co-design activity at hospital

To better understand how hospitals would better benefit from the app collected data, we conducted a co-design activity with data analysts at a hospital in Naples. Participants were given 50 words related to the problem domain and had to organize a data dashboard of their interest.

user testing

Screen Iterations

Along many rounds of in-person and remote user testings, the idea and screens evolved.

user testing user testing

Final Product

User experience

For demonstration purposes I coded the main interactions of the app in HTML and JavaScript, representing the user flow of a caregiver. The recorded video of the app interaction can be seen below.

Access Haven code on GitHub

Haven User-Flow

This screen flow demonstrate the user experience of navigating through the app.

haven flow

Road Map

Some potential features are expressed on the roadmap, such as expanding the service to paid caregivers, who would interact with the app differently than family caregivers for example.

haven flow