Ruby care is a noble attempt at helping patients by predicting and preventing diseases with the help of big data and information systems.
Ruby care aimed to build a platform for patients to store their data in a personal profile, so that in future they could easily share it with doctors, and even use it for online consultations. Not only that, but they were also hoping to automatically diagnose diseases by incorporating a machine learning algorithm. We were thrilled to be a part of such a great project, but the challenges we faced were far more fierce than any other projects.
Dr. Mahesh, the director of Ruby care came to us with a half-baked platform, which was made by a freelance software developer. It was not only a technical challenge anymore, but it also concerned the financial as well as the marketing aspect of Ruby care. Re-coding the whole thing was not a financially viable option -- neither was refitting all the data and formatting all of them to match our own ideas. We realized that we were way out of our comfort zone but little did we know that would bring out the best of us.
After a lot of brainstorming sessions, we finally decided we were going to refactor the code. So we manually went through every single line of the previous code, and carefully observed every tiny detail to preserve the functionalities and prevent data loss. Our team paved the way to success with an amazing combination of hard work and smart work.
We had to keep in mind that the target users are not actively involved in the technical field, so a simple yet aesthetically pleasing platform was what we needed. We also decided to restructure the system architecture by using more efficient methods.
We presented the mock-ups and carefully listened to the feedback from doctors and patients. The community of patients and medical professionals wanted a useful platform with all the features in their fingertips. Noone in the medical industry had enough time to search for a specific feature at the time of need. So we made all the features easy to find and made the platform as fast as possible.
For the diagnosis part, we embedded an AI which tracks and develops a diagnostic database, which grows larger day by day, making it much more accurate and useful.