3D Kinetic Typography | Jeff Wilkinson Design

Year

2023

Personal Medical Data Analytics Application

This is a slide deck from my final presentation of this material. For the following slides I'm going to upload my presentation notes, exactly as they were day-of. I've omitted a few of the less relevant slides. While these mostly don't contain process work, I think it's a good way to quickly get a general idea what this project was.

The tools that patients have at their disposal are outdated, and in many cases that can prevent them from receiving adequate treatment or even cause harm.

Our communication tool for pain starts out as a smiley face scale and never improves much beyond that.

When patients don’t feel they are adequately able to express their pain experience, or that their experience isn’t being listened to, they are less likely to be benefit from medical care, even it is the technically correct intervention. SO if you don’t believe you’re being heard, you don’t believe you can be treated. And that can have major quality of life impacts.

This project focuses on people suffering from chronic migraine. It’s a mysterious condition with a massive number of causes. Chronic Migraine sufferers are generally in some stage of a migraine 50% of the time. But more than that, I focused on migraine because it is invisible. The only real diagnostic tool available is the patient’s ability to communicate their experience. Not being experts, they often don’t know what to consider as symptoms, and both patient and doctor have to make speculative assessments about what may be a migraine trigger.
So they goal then is a tool that will allow self-knowledge and self-advocacy.
In section 1 I focus on the understanding and communication of migraine symptoms. How do we present migraine data in a way that is easy for a non-expert to read and understand, and how do we capture that data in the least intrusive way possible for someone who is in pain?
In section 2 I expand to focus on migraine triggers. Here, we’re using the sensors on mobile devices, synchronized with the data from section 1, to identify possible migraine triggers
In section 3 I focus on education — it’s not enough to capture data. Our user needs to understand it and be able to readily communicate it.

The primary theory I’m basing my framework on is Schiavenetto’s theory of Pain Assessment as a Social Transaction. The most important part here is the trust/scrutiny threshold where all parties bring in assumptions and limitations which impede communication. Together with Vessey’s Cognitive Fit Theory which sets aside a framework for problem-solving (the problem here being expressing a personal subjective experience in a quantifiable way.

And attempting to bypass some of this trust/scrutiny threshold by giving doctor and patient a shared visual and verbal language to communicate with.

Here’s the investigation model — generally these questions are moving forward in time through the patient’s side, preparing for their doctor visit.

Here’s our primary user, Ada. Main things to know
No inciting injury for her migraines
Fairly typical symptom profile, though not exactly
She’s using this app to prepare for her specialist appt

These are some early attempts trying to construct a shared visual language for a migraine episode

I ended up with something more resembling a graph, but simplified. The goal isn’t exactly in-depth data literacy, it’s getting our user to identify symptoms at a glance and changes in trends.

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This scenario video was done as part of my final thesis project for the masters of graphic and experience design program at NCSU. The work is the culmination of research investigations spanning the past year.

Key concerns in this investigation involved designing complex data visualizations for users who are often actively experiencing a migraine. On top of that the system needed modular hierarchies that could work with around 40+ datasets at once. I spent a lot of time weighing fidelity against accessibility -- while I'm not happy with all of the polish on the interface (especially the mobile), it accomplished many of its goals for being such a massive project.

Another goal was to allow patients to self-advocate in medical settings using their own data. Most migraine triggers can be tracked using phones and smart watches, but analyzing complex correlations is difficult. How then can we use machine learning to establish migraine trigger correlations, and educate non-expert patients on what their own data is saying?

Research-intensive Design

This project was about 4 months of research, followed by another 4 months of design exploration. A lot of that exploration is not currently shown here, but understand that each of these design iterations was following a specific line of questioning. For example, "how do you design an interface to be seen only in extremely low light?" resulted in delicate contrasts of blue-tones, with very intentional use of contrast and large buttons.

Full disclosure, I'm still figuring out how to explain this project to people who weren't there for the whole thing! It's been a winding path, pivoting often as necessitated by research constraints and the limited scope of a solo project. This started very differently, with augmented reality tools for expressing pain symptoms. You can see more of that original exploration here.

Personal Medical Data Analytics Application

Research-intensive Design

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