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COPD Design Sprint

 
 

Our sprint challenge was to improve the lives of people with chronic obstructive pulmonary disease or COPD, the fourth deadliest disease in the world.

COPD
Design Sprint

 
 
 
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Understanding COPD

The sprint started with an expert briefing on COPD from doctors, nurses, and scientists. We learned that every four minutes one person with COPD dies, that the people most at risk are seniors in developed nations and children in developing countries, and that exacerbations are the most common cause of death for people with COPD. 

 
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Journey Mapping

A journey map helped us quickly understand the main pain point for the patient—self-diagnosis. June, a 60-year-old retired teacher suffers from severe COPD. Now and then June’s chronic cough worsens. Like most people with COPD, June isn’t sure if her worsening cough is an exacerbation until she finds herself in a hospital bed. At least, this time, it wasn’t deadly. June could have taken her rescue pack and recovered without a hospital visit, but if it weren't an exacerbation, the pack would do more harm than good. To those with COPD, every cough is a literal matter of life and death. We decided this would be a good area to focus the rest of our sprint. 

 
 
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Visualizing Coughs

Once we had focused on a pain point, we brainstormed solutions, evolved them using lateral thinking and selected a cough detection concept to prototype. The idea was to distinguish coughs from one another and diagnose exacerbations early on.

The prototype is made up of two main parts machine learning software that listens passively for coughs using the mic of any phone, and a mobile interface. We learned that computers have a hard time distinguishing cough sounds. To work around it, we took the audio data of coughs and visualized it, then used existing image based recognition to predict exacerbations before they happened.

 
 
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Mobile Interface

The interface helps set up the software, alerts users when an exacerbation is detected and can also be used as a health journal to track other symptoms.

 
 
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Testing

With the interface mockups and some of the machine learning code complete, we were ready to hear what real people with COPD thought of our prototype. We selected 5 participants for testing and learned that while they found the idea promising, there was a gap we hadn't addressed in trust. Participants mentioned that they would require some form of reassurance before believing the diagnosis was accurate and risking their lives. We addressed the issue in subsequent tests by changing our flow and having their physician recommend the apps.

 
 

Conclusion

We presented the results of our 2-day design sprint at the ERS International Congress in Amsterdam to 15,000+ respiratory physicians and released the software open source.  

 
 

Sprint Team

Dave King, Dr. Douglas Newbury, Hemang Rishi, Irene England, Jun Kamei, Koraldo Kajanaku, Neil Highley, Phil Noonan, and Sarah Price.

This sprint was a collaboration between the COPD Foundation, Imperial College, and MIT Media Lab.