16 Establishment of a digital 'rare disease resource' for great ormond street hospital. (22nd November 2019) - Krastev, Georgi; Elston, David; Borch, Max Von; Bassi, Daiana; Conner, Sue; Fu, Yun; Mohamedally, Dean; Molyneux, Gemma; Roberts, Graham; Sebire, Neil
Abstract : Introduction: During medical training exposure to patients presenting with rare diseases may be uncommon. An educational resource that allows trainees to view clinical observations from patients with rare diseases would improve their knowledge and training experience, better preparing trainees to identify and recognise rare diseases in their future careers. Method: As part of a joint collaboration between GOSH and UCL computer science (CS) through the industry exchange network programme, CS students devised a game with the aim of correctly diagnosing a virtual patient by asking the least amount of questions. The application was developed using Django, a web application framework written in Python that employs a model-template-view (MTV) pattern. The client-side templates make use of Bootstrap to create fully responsive web pages. The application's back-end encapsulates the app's logic; i.e. the view and model layers, and interacts with the data persistency layer, a SQLite database. Additionally, the web app employs Azure's Face Detection API to flag, and prevent, the upload of potentially identifiable facial images. Results: The rare disease resource is a concept which would facilitate the development of a database of rare diseases for educational purposes. The database and web based user interface created allows medical professionals to upload anonymised medical cases for education purposes. Allowing users to view medical cases to improve understanding of disease manifestation, test results and care pathway. Conclusion: This website and user interface provide a prototype for the development of a web based tool to aid medical learning in a more flexible and interactive way.