Building a Typology of General Practitioner Consultations Prepared Via a Digital Medical Assistant

Deronsart

Aldebaran, France

Biography :

Nicolas Deronsart is a Data Scientist apprentice in the French startup Aldebaran, and he is also pursuing a Master’s in Machine Learning at the University of Lille. He is working in the R&D section on Machine Learning and Data Science concerns. Aldebaran is a company developing an intelligent medical assistant designed to optimize general practitioners’ time. By facilitating a customized questionnaire for patients before consultations, it generates detailed medical reports, enabling healthcare professionals to offer more tailored and effective care. This assistant adapts to various healthcare needs, significantly enhancing patient treatment and physician efficiency within the medical field.

Abstract :

Aldebaran’s intelligent medical assistant offers an innovative approach: involving patients in the preparation of their consultation by offering them the possibility of completing a personalized questionnaire before their appointment. Based on a knowledge base, the questionnaire transposes the logic of a medical interrogation into a plain-language questionnaire that adapts to the patient’s responses. The medical assistant then generates a summary report in medical language for the healthcare practitioner. Measuring the level of complexity of a consultation could enable the practitioner to better estimate the time needed for the appointment. Our study aims to identify the typology of consultations based on the reports generated, contributing to the future implementation of a system to calculate a composite score of syntactic and medical complexity.