- Covid-19: NOVA LINCS branch at the University of Évora develops solution to reduce service time on the SNS24 Line
- Mar 2020
The system called “SNS24 Scout”, which has been in development since January, allows a decrease of at least 5% in the time of each telephone call and an estimated increase of 50,000 telephone calls answered by the SNS24 Line, during one year.
"By the end of the year, we expect to have an application that has the ability to recommend the most appropriate clinical algorithm for each situation with an accuracy greater than 95%, bringing together conditions to be integrated into the current SNS24 application", said Paulo Quaresma.
According to Paulo Quaresma, "taking into account the current situation of the covid-19 pandemic and the strong pressure on the SNS24 Line, I believe that there are conditions to start this process before the end of this year".
The development of the project "Application of Artificial Intelligence Methodologies and Natural Language Processing in the SNS24 Screening, Counseling and Referral Service", in partnership with Shared Services of the Ministry of Health, E.P.E. (SPMS), is carried out by a multidisciplinary team of researchers from the University of Évora, NOVA Laboratory for Informatics and Computer Science (NOVA LINCS) and the Research Center for Mathematics and Applications (CIMA).
For the development of the SNS24 Scout, the team is applying techniques of Natural Language Processing and Machine Learning, developed specifically for the Portuguese language, as well as knowledge representation methodologies. In a first phase, it was necessary to "identify the most appropriate algorithms for a given set of symptoms, with adjustment for age and sex" and, for this, "a small prototype was developed, based on just three months of data from the SNS24, which demonstrated to have a very positive performance ", explained Paulo Quaresma.
After the creation of the prediction model, the system will be implemented in the SNS24 Screening, Counseling and Referral Service (TAE), with a decision support character, helping the nurse to select, in real time, the most appropriate clinical algorithm. The project team also includes researchers Irene Pimenta Rodrigues and Teresa Gonçalves.
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