CertiPair testimonial
ALIA SANTé
Discover the testimony of CertiPair, a collaborative base for short medical advice, which called on the expertise of Torus Medical to help it carry out its project.
Reading time: 2 minutes.
You have just left your medical appointment and yet you have already forgotten your doctor’s advice and/or prescriptions! Don’t worry, you’re not the only one.
95% of medical recommendations are not memorized.
Consultations that are too “short”
In France in 2017, 2 million consultations are carried out every day by general practitioners. In other words, that amounts to almost 22 17-minute consultations per day. (study conducted by Doctolib from May 1, 2016 to April 26, 2017). It is undeniable that since the COVID-19 health crisis, we can certainly believe that these figures have significantly increased. Among daily consultations, a majority is dedicated to medical monitoring, renewal of treatments as well as diagnosis. In order to care for as many patients as possible, general practitioners try to respect time slots per consultation. This is why these questions follow one another and some patients consider them too short, not having the time to fully understand, remember and ask their questions to the healthcare professional. On the doctor’s side, the lack of time for his patient can influence the relevance of the proposed treatment/monitoring.
A solution : CertiPair
CertiPair is a SAAS collaborative database that allows healthcare professionals to semi-automatically send recommendations, alerts or advice to their patients by SMS after the ‘too short’ consultation. For example, the user can receive analysis reminders, advice on medical treatments or pre-surgical protocols.
Why SMS?
The WHO says that SMS affects 85% of the world population compared to 35% for Chatbots and mobile applications.
So that the professional can access in just a few clicks the most relevant message (in the secure collaborative database) for a given problem, CertiPair needed to integrate an artificial intelligence algorithmic solution. The goal being to optimize the message search system, through the symbiosis between different information (recognition of text fields, identification of keywords in identified contexts, relevance score based on the expertise of health professionals and frequency of use in particular).
We needed to integrate an artificial intelligence algorithmic solution whose aim is to optimize the message search system, through the symbiosis between different pieces of information.
