Concept: Visual electronic health record application that tracks progress of health. In addition to the visual 3D component of the design, the application was to track real time overall wellness in relationship to lifestyle. Diet, exercise – syncing to Fitbit or Apple watch, medications and social time.
The 3D concept was taken from Microsoft Garage Project Innereye Eye. An artificial intelligence tool that uses machine learning and computer vision to allow radiologists to make cancer treatment more targeted and effective.
Case Example: Visual ability to monitor Multiple Sclerosis lesions on brain and spinal cord in relationship to physical symptoms. Loss of motor control in the right leg for example, or change of vision in the right eye in relationship to areas of the brain that are responsible for those functions.
Conclusion: Regarding Multiple Sclerosis, lesions on the brain and spinal cord have no relationship to symptoms.
“It’s a good concept but the lesions on the brain and spinal cord are asymmetrical to symptoms expressed in the body. I mean, I get why you’re chipping at it. It’s hard to understand what you can’t see.” –
Michael J. Persenaire M.D., Neurologist, University of Washington
Moving forward: Visual electronic health records and dimensional communication could provide a more effective way to communicate, empowering patients to see and interact with their health in a more understandable way (see bottom right, patient pain diagram).
Things to consider: Business model and data. It takes a lot of data for machine learning to recognize patterns (variances in images) and create a computer vision model. MRI’s are taken in “slices” from a horizontal and vertical plane. Reconstructing an accurate model from piece meal MRI images, including volume, could prove to be challenging.
Additionally, empathy and user experience. Intuitive patient know-how or affordances to navigate a new technology and how to make a complex interaction super easy, intuitive and enjoyable while being beautiful. And of course, compliance and trust. Understanding how machine learning and AI in the medical field could be weaponized.
Findings: Small but strategic details can elevate an environment. Physicians and patients don’t always speak the first same language be it native or technical. Most people however understand symbols and images. Highlighting a problem area in a virtual red or placing an upset emoji face could alleviate communication insecurities.