Healthtech Innovation

In the Health area, there are many opportunities and insights that can be recognized by applying techniques of artificial intelligence. Our project with Finep and the Ministry of Science, Technology and Innovation, with resources from the FNDCT, seeks to innovate the sector of process analysis to make it even more assertive and agile with the use of Machine Learning.

CHALLENGES

The main challenge is to make the process of analyzing administrative challenges to SUS reimbursement automated, agile and simplified, maintaining a large base of data intelligence so that there is also performance in learning and in the responses to be provided.

These objections are received every month and the ANS needs to evaluate and grant or reject them. Today, all of this is made by hand by ANS technicians, making the activity slow due to the large number of processes.

HOW WE HELP

First, by creating a legal process analysis platform with a Machine Learning algorithm, developed with the help of NLT libraries using Python as a programming language, seeking to bring agility and simplification the analysis of legal processes.

With the implementation of the platform, the focus is to have a initial reduction of 60% in workload of technicians manually. As the Artificial Intelligence algorithm learns from specific cases, performance will grow quickly and assertively.

This platform will be able to: analyze, interpret, present a preliminary response to technicians and, depending on the level of complexity, will automatically respond to these challenges.

Thus, the stored data will be processed in large volumes (big data) hosted on servers cloud allowing climb, when necessary, the resources of: storage and memory and processor on the server. In addition, redundancies and clustering will be generated to greater performance.