AtemWeise - a data based asthma app

AtemWeise - a data based asthma app

AtemWeise - a data based asthma app

AtemWeise - a data based asthma app

Client :

Self-initiated

Client :

Self-initiated

Client :

Self-initiated

Client :

Self-initiated

Industry:

Digital health

Industry:

Digital health

Industry:

Digital health

Industry:

Digital health

Year:

2023

Year:

2023

Year:

2023

Year:

2023

Role:

UX designer

UX researcher

Role:

UX designer

UX researcher

Role:

UX designer

UX researcher

Role:

UX designer

UX researcher

Problem

Problem

Problem

Problem

Both patients and doctors would benefit from a better diagnostic tool. Recent development in data science and machine learning would make following the progression of a patient’s Asthma more accurate and allow doctors to offer better treatment.

Asthma patients do not often engage with digital tools although there are some available on the market. Offering an improved way to keep track of all Asthma related issues in one tool would benefit patients.

Both patients and doctors would benefit from a better diagnostic tool. Recent development in data science and machine learning would make following the progression of a patient’s Asthma more accurate and allow doctors to offer better treatment.

Asthma patients do not often engage with digital tools although there are some available on the market. Offering an improved way to keep track of all Asthma related issues in one tool would benefit patients.

Both patients and doctors would benefit from a better diagnostic tool. Recent development in data science and machine learning would make following the progression of a patient’s Asthma more accurate and allow doctors to offer better treatment.

Asthma patients do not often engage with digital tools although there are some available on the market. Offering an improved way to keep track of all Asthma related issues in one tool would benefit patients.

Both patients and doctors would benefit from a better diagnostic tool. Recent development in data science and machine learning would make following the progression of a patient’s Asthma more accurate and allow doctors to offer better treatment.

Asthma patients do not often engage with digital tools although there are some available on the market. Offering an improved way to keep track of all Asthma related issues in one tool would benefit patients.

Solution

Solution

Solution

Solution

An application for diagnosing and analyzing asthma patients’ symptoms, finding potential triggers for attacks, evaluating the efficiency of a therapy, and generally determining whether a patient’s asthma progress is positive or negative, with the help of machine learning. The App includes a symptom diary, weather and allergens daily data, medication counter, appointment reminder, a symptom analysis, and a symptom report.

An application for diagnosing and analyzing asthma patients’ symptoms, finding potential triggers for attacks, evaluating the efficiency of a therapy, and generally determining whether a patient’s asthma progress is positive or negative, with the help of machine learning. The App includes a symptom diary, weather and allergens daily data, medication counter, appointment reminder, a symptom analysis, and a symptom report.

An application for diagnosing and analyzing asthma patients’ symptoms, finding potential triggers for attacks, evaluating the efficiency of a therapy, and generally determining whether a patient’s asthma progress is positive or negative, with the help of machine learning. The App includes a symptom diary, weather and allergens daily data, medication counter, appointment reminder, a symptom analysis, and a symptom report.

An application for diagnosing and analyzing asthma patients’ symptoms, finding potential triggers for attacks, evaluating the efficiency of a therapy, and generally determining whether a patient’s asthma progress is positive or negative, with the help of machine learning. The App includes a symptom diary, weather and allergens daily data, medication counter, appointment reminder, a symptom analysis, and a symptom report.

Process

Process

Process

Process

At this stage I finished performing the 1st iteration in the design thinking process. I started with desk research and competitive analysis. After my research I analyzed the user context : generated user groups and wrote user as-is scenarios, user tasks model and user personas.

At the end of the user context analysis I extracted 31 user requirements that were mostly related to 4 main user tasks.

Based on those user tasks I began ideation with user flow diagrams for each of the tasks. Based on the user flows I started wireframing. I prefer to use pen and paper as it is quicker and more intuitive for me. Since I wanted to test my prototype as soon as possible, I used my paper wireframes to generate a digital paper prototype with Figma which I used for remote user testing. I tested with 5 users (and one pilot tester). I wrote task scenarios and created a separate flow for each of the tasks. The tests were observational and were recorded for further analysis.

After testing I started to work on Hi-fidelity wireframes and prototype.

At this stage I finished performing the 1st iteration in the design thinking process. I started with desk research and competitive analysis. After my research I analyzed the user context : generated user groups and wrote user as-is scenarios, user tasks model and user personas.

At the end of the user context analysis I extracted 31 user requirements that were mostly related to 4 main user tasks.

Based on those user tasks I began ideation with user flow diagrams for each of the tasks. Based on the user flows I started wireframing. I prefer to use pen and paper as it is quicker and more intuitive for me. Since I wanted to test my prototype as soon as possible, I used my paper wireframes to generate a digital paper prototype with Figma which I used for remote user testing. I tested with 5 users (and one pilot tester). I wrote task scenarios and created a separate flow for each of the tasks. The tests were observational and were recorded for further analysis.

After testing I started to work on Hi-fidelity wireframes and prototype.

At this stage I finished performing the 1st iteration in the design thinking process. I started with desk research and competitive analysis. After my research I analyzed the user context : generated user groups and wrote user as-is scenarios, user tasks model and user personas.

At the end of the user context analysis I extracted 31 user requirements that were mostly related to 4 main user tasks.

Based on those user tasks I began ideation with user flow diagrams for each of the tasks. Based on the user flows I started wireframing. I prefer to use pen and paper as it is quicker and more intuitive for me. Since I wanted to test my prototype as soon as possible, I used my paper wireframes to generate a digital paper prototype with Figma which I used for remote user testing. I tested with 5 users (and one pilot tester). I wrote task scenarios and created a separate flow for each of the tasks. The tests were observational and were recorded for further analysis.

After testing I started to work on Hi-fidelity wireframes and prototype.

At this stage I finished performing the 1st iteration in the design thinking process. I started with desk research and competitive analysis. After my research I analyzed the user context : generated user groups and wrote user as-is scenarios, user tasks model and user personas.

At the end of the user context analysis I extracted 31 user requirements that were mostly related to 4 main user tasks.

Based on those user tasks I began ideation with user flow diagrams for each of the tasks. Based on the user flows I started wireframing. I prefer to use pen and paper as it is quicker and more intuitive for me. Since I wanted to test my prototype as soon as possible, I used my paper wireframes to generate a digital paper prototype with Figma which I used for remote user testing. I tested with 5 users (and one pilot tester). I wrote task scenarios and created a separate flow for each of the tasks. The tests were observational and were recorded for further analysis.

After testing I started to work on Hi-fidelity wireframes and prototype.

To see the full project presentation, please click here

To see the full project presentation, please click here

Paper prototype

Paper prototype

Paper prototype

Paper prototype

Clickable Prototype

Clickable Prototype

Clickable Prototype

Clickable Prototype

Let’s Connect

Let’s Connect

Let’s Connect