There is an explosion of digital media being created by healthcare professionals who use their personal mobile devices to document and communicate physical findings or documents with peers- I experienced this first hand as a vascular surgeon working with my team of medical students, residents, fellows, nurse practitioners and peers. Whether it is a photo of a post-operative wound from a patient in the ER, a CT scan of a patient being referred from a distant hospital or a photo of a preliminary ultrasound report. What fascinated me was how EFFICIENT this form of communication/collaboration was! It takes seconds to whip out a phone and take a photo and milli-seconds to interpret the image- imagine the time it would take to communicate the same information in writing! These images are packed with valuable data and with the current workflow, all this valuable patient data is being lost in the ether on people's personal phones. In addition we want to disrupt the current workflow for image acquisition in many clinical settings- digital camera->SD card upload-> file transfer-> print-> scan! Even EPICs current solution in Haiku only takes dumb JPEGS and stores photos in its media tab next to consents, as well as other documents.
We have made it our mission to 1) capture all this valuable data with medical grade contextual data, 2) get it off of your personal phone, 3) make it compliant, 4) dramatically improve the viewing and sharing experience, and 5) teach computers through deep learning to eventually generate structured reports from photos.
We have a lot to build! Currently BodyMapSnap (v1.4) available in the app store takes photos using our unique 1-tap-4-action UI. We add time/date stamp, laterality, body location tag as well as the ability to add searchable text. All photos are stored in the cloud and NOT on your phone.
Version 3.0 is 90% built (see screen shots) and is the equivalent of what the Model S was for Tesla. It will be healthcare production ready! Each photo session is treated as a "study" and associated with an accession number (radiology imaging term) and can be easily integrated into any enterprise PACS system or VNA (vendor neutral archive). We have added our own in-app contextual chat service, in-app notifications, a measurement tool to be able to measure photos (think wound sizes/rashes/etc), auto-color correction, and OCR (image->text) document capabilities. In addition, all the administrative auditing tools/logs are present.
Version 3.0 is being build using Objective C for native iOS on the front end. Our image analysis measurement algorithm was built using C#.
Entrepreneurship is hard!! In addition we have had a lot of technical challenges managing poor connectivity and low bandwidth conditions as we store all our data on our secure server.
We are proud of our product and our vision. We have really differentiated ourselves by focusing on being 100% medical grade.
We have learned to be persistent and to focus on our vision.
We are extremely excited to add video capabilities, get our FDA 510K approval, integrate with light.co's camera (amazing!), add detailed body maps of the face, hands, feet and partner with some large players in healthcare that believe in transforming healthcare!
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