- The existing visual reference was used as a base to explore a divergence of ideas for the arrangement of visual information.
- Each approach was sketched out and labeled according to the way the scores are displayed.
- Every iteration helped understand how scale, color, and graphical elements, influence how scores are read and interpreted.
- For instance, which color conveys a better sense of urgency? Red or yellow?
- Does adding a line graph show improvement or decline in the score over time?
The new concepts included iterations of the individual score blocks that use prominent colors to highlight differences in score severity.
- 2 rounds of interviews with 2 Community Neurologists, and 2 with Primary Care Practitioners who have worked with PD patients.
- Defined their product needs; what data and trends are important to display?
- How do they see the concepts working in their anticipated use case scenarios?
Through interviews, we learned that clinicians are looking for a tool that directs PD care towards early detection and symptom-tracking prediction while supplementing knowledge for their patients
- Clinicians want to see subtleties in the initial stages of symptom tracking.
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It is critical to bring the patient and provider around a common set of data.
- Clinical scores from UPDRS must be supported by validated results from patient testing.
Each mockup tested the viability of score navigation testing static clickable items against hover state elements.
Graphs for data visualization, colors to highlight severity, and plain language to interpret scores, were preferred by all the interviewed clinicians
Specific feature-focused insights included:
- Icons are not useful and rather distracting; they do not provide much meaning.
- “Alerts are great for more critical symptoms like gait”.
- Tougher to interpret graphs without color, especially when scores are collapsed.
- Suggestion boxes for dosage and medication changes, high-risk symptoms, and extreme disease progression.
- State changes in graphs to reveal a timeline of diagnosis, medication changes, and increases/decreases in score over time.
- Diagnosis predictions based on a data set that compares an age-related cohort of patients.
Data must not just be visualized accurately but be insightful, actionable, and drive change.
- Complex data carries meaning when it is simplified to reflect the language of both patients and clinicians.
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Contextual research (in this case, knowledge about Parkinson’s), is essential in not only designing a usable product but also one that resonates with the users’ lived experiences.
- Remaining humble when receiving feedback is crucial to learn, grow, and make progress.
Read more about my experience here