FutureType : Word completion for medical reports

The current research contributes to the development of FutureType, a word completion tool for medical reports, from two perspectives. First, we evaluate the potential of a set of meta features about the patient, author and the report itself to improve FutureType's prediction capacity. Second, we conduct a large scale split test involving the collection of keystroke data from ten customer organizations of Nedap Healthcare, involving 7062 healthcare professionals and spanning 14 and a half weeks to investigate the transferability of instrinsic metrics of language model performance to evaluation with real end users. Our results pave the first steps towards a meta-enriched FutureType as we find distinctive power regarding vocabulary choices for three meta features: the healthcare sector a report originates from, the type of the report and the expertise of the author of the report. The results from the split test advocate a holistic approach to the evaluation of text prediction applications that takes into account both, the system's utility (i.e., the quality of its predictions) and its usability.