The Challenge
In medical research, data is king. The research team in the Dermatology Department at Stanford School of Medicine studying Epidermolysis Bullosa (EB), a genetic tissue disorder that causes blisters or tears in fragile skin, found itself struggling to properly measure and categorize patient wound sizes, develop a reliable timeline for healing, and identify endpoints for future studies—all critical elements of its work.
With many patients decentralized and without regular access to expensive cameras and diagnostic equipment, the department struggled to maintain the continuity of data needed to drive research and draw meaningful conclusions.
“Before we had Tissue Analytics, we were only seeing patients maybe once or twice a year,” said Nicki Harris, Assistant Clinical Research Coordinator in the Dermatology Department at Stanford School of Medicine. “That was the only time we could effectively check on their wounds and take photos.”
The Solution
The solution needed to be accessible, adaptable, and easy to use. Tissue Analytics, a Net Health company, delivered. Tissue Analytics is a pioneering developer of automated mobile wound and skin imaging and predictive analytics solutions.
The company’s flagship product allows patients to download a secure app to their smartphone that the patient can use to send in pictures of their wound, as well as critical data like the degree of pain and itching during wound dressing changes. Images and data are sent securely to the provider’s dashboard, where the system automatically traces the wound, measures and maps the wound, and provides key metrics to the database.
Benefits of the product include:
- A remote solution that allows patients to submit pictures and updates on their wounds regularly without needing to go into a doctor’s office or clinic
- 2D and 3D automated measurement technology that reduces measurement errors by over 40%
- Integration with Net Health’s WoundExpert, as well as a robust suite of integrations with many well-known hospital EHR systems
- Advanced machine learning algorithms critical to the expansion of predictive analytics
The Results
Thanks to Tissue Analytics, the research team in the Dermatology Department at Stanford School of Medicine studying Epidermolysis Bullosa (EB) now receives weekly uploads of photos from patients enrolled in the study. Because patients can send in their own photos through the easily accessible app, the team isn’t limited by the patient’s geographical location or their access to expensive imaging technology.
The continuity of data, automated technology, and ease of use of the product have empowered the team to increase the size of its study through effective decentralized clinical research.