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68 Development of a clinical training application for rare paediatric disease
  1. Shirajul Hussain1,
  2. Anna Rolland1,
  3. Daiana Bassi2,
  4. Sue Conner2,
  5. Yun Fu1,
  6. Dean Mohamedally1,
  7. Gemma Molyneux2,
  8. Graham Roberts1,
  9. Neil Sebire2
  1. 1UCL
  2. 2GOSH


Background Modern technology could be implemented to enhance clinical training. GOSH is a hospital specialising in the treatment of rare childhood diseases that can be hard to diagnose and treat. To support training of medical students to understand more about rare diseases we devised a game to support learning.

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. This application employs a three-tier, presentation, logic and data, software architecture pattern. The presentation tier consists of a mobile application developed in Ionic4, an open source, cross-platform toolkit for developing mobile and desktop apps that uses standard web technologies; HTML, CSS and JavaScript. The logic tier, which contains the application’s functional business logic, was built in Node.js and consists of the RESTful APIs the mobile app uses to access the data. The data tier comprises of a PostgresSQL database to store user details and answers, patient cases and questions.

Results Trainee doctors are provided with simple demographic details and the reason for the medical assessment. The player can then request results of standard examinations such as blood test, x-ray, MRI etc. The game ends when the player selects the diagnose button and submits an answer, following which the correct diagnosis is revealed, with information about the condition. A working proof of principle was developed which could be expanded to include person, department or organisation specific leader boards as part of future development.

Conclusion An application to allow gamified medical learning on mobile devices for rare paediatric conditions has been developed with potential for expansion across a range of clinical settings and services.

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