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Hospital staff chatbot built from training materials and exam answer keys

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A team built a bilingual English and Arabic chatbot to answer staff questions about the InterSystems TrakCare hospital information system. Their training corpus came from scraping Moodle assessment print-outs, which contained correct answers, yielding 433 verified question-answer pairs. They also transcribed 43 training videos with an open speech-to-text model and distilled them into topic files. The system uses a hand-built BM25 retrieval engine with source-trust weighting and an Arabic normalization and clinical synonym layer rather than a vector database. The chatbot is available as a live demo on a training environment without patient data, running on free open models.

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