License: Apache License, 2.0
Mailing List: https://groups.google.com/forum/#!forum/raxa-gsoc/joinRaxa is an organization dedicated to creating a free open-source healthcare information system and an electronic medical record for mostly poor, sometimes illiterate, patients and their overworked providers who are always in need of reliable health information accessible on mobile platforms. We are focused on India and developing countries, but our system is applicable across the globe. On a day-to-day basis, some of the major improvements promised by our system include providing (1) effective access to and ownership of health information by patients, including by voice (2) patient health-seeking behavior decision support and provider clinical decision support and (3) communication between doctors, public health specialists and mobile community health workers. Our conviction is that electronic health systems are the way of the future, and that their adoption is even more vital, impactful, and scalable in developing countries.
- Doctors Near Me Doctors Near Me is a mobile app implemented using Sencha Touch, that provides the functionality to users to search, get directions, find more details about hospitals, doctors, pharmacies and allows them to view on a map, sort and filter according to various parameters. If the user is a registered user he can like/comment about the places after logging in. The user can mark his favorite locations. The app will support language translation.
- Project: Voice/SMS Server for Patient Follow Ups A proposal to enhance the present 'Raxa_Voice_SMS' module to support followups via call and SMS as per user preference. Additional improvements such as Automatic Speech Recogniotion (ASR), local language translation as well as call recording functionality are proposed.
- Raxa Drug Recommendation System India has only one doctor per 1700 citizens. There is need for system that can suggest drug for a diseased patient given his health conditions. Raxa Drug Recommendation is project based on Machine Learning to suggest patient drug on the basic of past medical records. The project employs a UMLS meta-thesaurus and a generalized algorithm that can take data from any medical records database and can incorporate any number of features related to medical record like age, locations etc.
- That's What Doctor Said Doctor’s medical prescriptions are difficult to understand for not only semi literate people but also sometimes for the learned ones. The proposed system would automatically convert the complex cryptic prescription to layman text for quick understanding with added functionalities of preferred language and more information about the drug and the diagnosis.