A deployable mobile medical application is presented that employs a smartphone camera, patient input, internet connectivity, and cloud-based image processing techniques to document and analyze physiological characteristics of hands in osteoarthritis (OA) patients. The application performs digital image processing that spatially calibrates the image, locates hand fiduciary features, and quantifies hand features to identify abnormal distal and proximal interphalangeal joints. The algorithm determines the finger centerlines and joint coordinates. From these anatomical fiduciary points, it measures the width of fingers, location and size of joints, and finger joint angulation. The diagnostically relevant features measured by the mobile application can be applied to current diagnostic protocols such as the American College of Rheumatology (ACR) criteria for OA. Based on the results from a pilot study, the mobile application was modified to include interactive user guidance built into the smartphone. This app makes improvements on the algorithm that validate the image quality and makes the algorithm less dependent on precise capture conditions. Based on clinical feedback, a web-based portal and dashboard for advanced analysis was developed and presented. Clinicians, researchers, and patients can use this to explore relationships between pain, treatment, environmental parameters, and lifestyle factors.