CLAS App ML

CLAS App ML is to train writing skills of hospital discharge letters.

This is a multi-language (ML) update of the CLAS App original design by Bridget Maher from the School of Medicine at University College Cork, Ireland. The current version has an improve counting mechanism and has been translated from English to Spanish, Catalan and German languages within the European project PATIENT (www.patient-project.eu).

The CLAS App ML aims to train good written communication skills as they are essential to the practice of medicine and avoidance of medical error. The hospital discharge letter is probably the most important of all written communications between hospital and General Practitioner (Family Doctors). However, discharge letters vary greatly in quality, structure, cohesion, and ‘readability’. Most discharge letters are written by junior doctors and frequently omit important information. The School of Medicine at University College Cork developed a comprehensive check-list i-phone application to improve the quality of hospital discharge letters.

The CLAS scale lists the key elements of a discharge letter - reason for admission, investigations, results, diagnosis, problem list, medications, management plan, name and contact details of doctor writing the letter etc. There are 4 pages of items, divided into various sections. Either a section heading or an individual item can be ticked. Most items score 1, but some items of particular importance, such as medication, score higher. Total CLAS score is 50. At the end, the user is shown his total score and can swipe to see a list of unchecked items.

By using the CLAS application as a point-of-practice reference tool, doctors and medical students can improve their letter-writing skills, decrease the risk of medical error and improve patient safety. With the new version CLAS App ML we aim to provide a standard within Europe for the structure of hospital discharge letter. Therefore, the App has been translated to 3 new languages.