Journey Map for storybook!
Credit: Claire Melvin

Journey Map for storybook!

Credit: Claire Melvin


Expertise and Experience

—Sara Kashani


Insights, Alums, and New Directions

UPDATES: The A-team (A for Asthma!) has been working on rapid prototyping. We’ve focused on a storybook model to address the following question, which was developed after months of user research and reviews of the clinical literature:

How might we clarify or facilitate a child’s expression of their asthma symptoms so that their parents may know what actions to take for their care?

The storybook would feature colorful pictures and appeal to children between the ages of 3 and 6. We wanted to incorporate a “choose your own adventure” type of feature, so that children could decide what story line to follow, depending on how they felt that day and what symptoms they were experiencing. At the same time (and more importantly), the book would serve as a teaching tool to allow for better communication between children and parents (which addresses our question above).

So far, we’ve visited the children’s section of Barnes and Noble (see previous post) and dedicated a meeting to developing quick prototypes to get things started. This week, we’ve been trying to develop some more workable prototypes. Sensing some slowed momentum, I wanted to reach out to someone with a lot of design experience, especially with projects geared towards kids, and to try to get some insight—perhaps we could find some new meanings and directions for our project in this way.

So yesterday, I got the chance to talk to Hannah Chung, one of the original DFA co-founders, who along with another DFA alum (Aaron Horowitz), is currently working on their project, Jerry the Bear, at the Betaspring accelerator. Jerry’s ancient relative to other projects—he followed soon after DFA’s inception at Northwestern in 2009; I first met Hannah during Summer Fellows 2010. I remembered that she had worked on another children’s health project for DFA: Color Me Brave, a coloring book for kids with post-traumatic stress disorder (PTSD). I figured there might be parallels between the design process for Color Me Brave and Hannah’s other projects that could help out the A-team. 

After getting updates on Jerry (he’s doing very well), Hannah and I got to talking about the A-Team’s progress and our attempts at rapid prototyping. In a nutshell, this is what she recommended: take a step back and create a journey map. For example, we could match the symptoms that we’ve researched with how children actually communicate about those symptoms with their parents and others. In other words, our rapid prototyping could benefit greatly from some highly-focused user research that links what we’ve learned and studied to what words and phrases (in the real world) actually flow back and forth between children who have asthma and their parents.

Hannah and I talked about some other things, too, not all of them about journey maps, but what I did take away from our conversation is something that I’m going to term “translational design,” a way for me to describe applied design using the language of one of the hottest fields in medical science today. “Translational medicine,” currently a popular undertaking for clinicians, policymakers, and scientists, describes how we can take clinical or basic science research findings, combine them with what we know about our society and its people, and use all that insight to treat and/or improve the health of patients in some way (e.g. via new drugs, new devices, or even new systems—all for therapeutic benefit; the movement of biological findings in the lab to the market, or from the proverbial “bench to bedside”). 

Analogously, during this stage of our project, “translational design” can help us map out all the medical and pediatric asthma-related research we’ve accumulated in a way that can show us how children actually communicate their symptoms with their parents. This way, our storybook will be better informed, both in terms of the science/symptoms and the communications/reality. Ultimately, we too would like to translate our months of research into a workable solution that addresses our question way up there.

I’m excited to see what new directions our project might take once we start developing a journey map for our users’ experiences! 

—Sara Kashani

Next Steps: We want to create a journey map to capture what’s actually said between parents and their children with asthma. We’re looking for anyone whose got a child with asthma to help us with this part—please contact us if you’d be willing to give us your input! We can even honor or feature you and/or your child on our blog if you’d like (but only with your permission, of course). 


Quick update and what’s next

We went to Barnes and Nobles for our meeting, which turned out to be quite fun. So we are thinking we should mix it up in the future as far as meeting location goes.

We read a few story books to get a feel of what writing a children’s book for kids with age ranging from 3-8. One of the few characteristics that we thought of include:

  1. Short and snappy sentences
  2. Personification of animals are usually used
  3. Rhyming
  4. Conflicts and resolution of conflicts
  5. Others (we liked using baby elephant as the protagonist)

Next steps:

  1. Doing more research on:
  • Common symptoms
  • Diagnosis procedures

Claire, if you want to add to any of these pointers, since you took notes, whereas i’m just recalling from what I can remember, please do!

-David

Claire’s Nuggets:

  • Repetitive
  • Story does not always make sense or does not seem predictable
  • Has a lesson
  • Sometimes rhymes
  • Nonsensical
  • Frustration
  • Comedic for adults too

yaydfa,

Claire


I made these to prepare for our presentation. Just to show what we have done thus far, where we are now and what we are doing next.

Also we can use this software to make our diagnosis flowchart!


Comment enabled!

I enabled comment for this site! You are welcome to post any relevant information or questions for our project!

THANKS!


How do we find and classify the factors that explicitly lead to asthma attacks?
lawyowyou:

 In the 1970s, [Lee] Goldman got involved with a group of mathematicians who were very interested in developing statistical rules for telling apart things like subatomic particles. Goldman wasn’t much interested in physics, but it struck him that some of the same mathematical principles the group was using might be helpful in deciding whether someone was suffering a heart attack. So he fed hundreds of cases into a computer, looking at what kinds of things actually predicted a heart attack, and came up with an algorithm - an equation - that he believed would take much of the guesswork out of treating chest pain. Doctors, he concluded, ought to combine the evidence of the ECG with three of what he called urgent risk factors: (1) Is the pain felt by the patient unstable angina? (2) Is there fluid in the patient’s lungs? and (3) Is the patient’s systolic blood pressure below 100?
 For each combination of risk factors, Goldman drew up a decision tree that recommended a treatment option. For example, a patient with a normal ECG who was positive on all three urgent risk factors would go to the intermediate unit; a patient whose ECG showed acute ischemia (that is, the heart muscle wasn’t getting enough blood) but who had either one or no risk factors would be considered low-risk and go to the short-stay unit…and so on.
 Goldman worked on his decision tree for years, steadily refining and perfecting it. But at the end of his scientific articles, there was always a plaintive sentence about how much more hands-on, real-world reserach needed to be done before the decision tree could be used in clinical practice. As the years passed, however, no one volunteered to do that research - not even at Harvard Medical School, where Goldman began his work, or at the equally prestigious University of California at San Francisco, here he complete it. For all the rigor of his calculations, it seemed that no one wanted to believe what he was saying, that an equation could perform better than a trained physician.
 But [Brendan] Reilly shared none of the medical community’s qualms about Goldman’s findings. He was in a crisis. He took Goldman’s algorithm, presented it to the doctors in the Cook County ED and the doctors in the Department of Medicine, and announced that he was holding a bake-off. For the first few months, the staff would use their own judgement in evaluating chest pain, the way they always had. Then they would use Goldman’s algorithm, and the diagnosis and outcome of every patient treated under the two systems would be compared. For two years, data were collected, and in the end, the result wasn’t even close. Goldman’s rule won hands down in two directions: it was a whopping 70 percent better than the old method at recognizing the patients who weren’t actually having a heart attack. At the same time it was safer. The whole point of chest pain prediction is to make sure that patients who end up having major complications are assigned right away to the coronary and intermediate units. Left to their own devices, the doctors guessed right on the most serious patients somewhere between 75 and 89 percent of the time. The algorithm guessed right more than 95 percent of the time. For Reilly, that was all the evidence he needed. He went to the ED and changed the rules. In 2001, Cook County Hospital became one of the first medical institutions in the country to devote itself full-time to the Goldman algorithm for chest pain, and if you walk into the Cook County ER, you’ll see a copy of the heart attack decision tree posted on the wall. 

How do we find and classify the factors that explicitly lead to asthma attacks?

lawyowyou:

In the 1970s, [Lee] Goldman got involved with a group of mathematicians who were very interested in developing statistical rules for telling apart things like subatomic particles. Goldman wasn’t much interested in physics, but it struck him that some of the same mathematical principles the group was using might be helpful in deciding whether someone was suffering a heart attack. So he fed hundreds of cases into a computer, looking at what kinds of things actually predicted a heart attack, and came up with an algorithm - an equation - that he believed would take much of the guesswork out of treating chest pain. Doctors, he concluded, ought to combine the evidence of the ECG with three of what he called urgent risk factors: (1) Is the pain felt by the patient unstable angina? (2) Is there fluid in the patient’s lungs? and (3) Is the patient’s systolic blood pressure below 100?

For each combination of risk factors, Goldman drew up a decision tree that recommended a treatment option. For example, a patient with a normal ECG who was positive on all three urgent risk factors would go to the intermediate unit; a patient whose ECG showed acute ischemia (that is, the heart muscle wasn’t getting enough blood) but who had either one or no risk factors would be considered low-risk and go to the short-stay unit…and so on.

Goldman worked on his decision tree for years, steadily refining and perfecting it. But at the end of his scientific articles, there was always a plaintive sentence about how much more hands-on, real-world reserach needed to be done before the decision tree could be used in clinical practice. As the years passed, however, no one volunteered to do that research - not even at Harvard Medical School, where Goldman began his work, or at the equally prestigious University of California at San Francisco, here he complete it. For all the rigor of his calculations, it seemed that no one wanted to believe what he was saying, that an equation could perform better than a trained physician.

But [Brendan] Reilly shared none of the medical community’s qualms about Goldman’s findings. He was in a crisis. He took Goldman’s algorithm, presented it to the doctors in the Cook County ED and the doctors in the Department of Medicine, and announced that he was holding a bake-off. For the first few months, the staff would use their own judgement in evaluating chest pain, the way they always had. Then they would use Goldman’s algorithm, and the diagnosis and outcome of every patient treated under the two systems would be compared. For two years, data were collected, and in the end, the result wasn’t even close. Goldman’s rule won hands down in two directions: it was a whopping 70 percent better than the old method at recognizing the patients who weren’t actually having a heart attack. At the same time it was safer. The whole point of chest pain prediction is to make sure that patients who end up having major complications are assigned right away to the coronary and intermediate units. Left to their own devices, the doctors guessed right on the most serious patients somewhere between 75 and 89 percent of the time. The algorithm guessed right more than 95 percent of the time. For Reilly, that was all the evidence he needed. He went to the ED and changed the rules. In 2001, Cook County Hospital became one of the first medical institutions in the country to devote itself full-time to the Goldman algorithm for chest pain, and if you walk into the Cook County ER, you’ll see a copy of the heart attack decision tree posted on the wall. 


Brainstorming result for the our newly revised question! Way to go team! Also, Sara, welcome back to DFA!

Brainstorming result for the our newly revised question! Way to go team! Also, Sara, welcome back to DFA!


Mock-up for our team logo!
(Result from my extreme bordem with school)

Mock-up for our team logo!

(Result from my extreme bordem with school)


How might we clarify or facilitate a child’s expression of their asthma symptoms so that their parents may know what actions to take for their care.