{"id":2253,"date":"2024-12-13T11:00:20","date_gmt":"2024-12-13T14:00:20","guid":{"rendered":"https:\/\/paipe.com\/?p=2253"},"modified":"2026-08-17T10:15:08","modified_gmt":"2026-08-17T13:15:08","slug":"healthtech-innovation-how-machine-learning-makes-process-analysis-in-healthcare-more-agile","status":"publish","type":"post","link":"https:\/\/paipe.com\/en\/blog\/inteligencia-artificial\/inovacao-healthtech-como-o-machine-learning-torna-a-analise-de-processos-na-saude-mais-agil","title":{"rendered":"Healthtech innovation: how Machine Learning makes process analysis in healthcare more agile."},"content":{"rendered":"

In the healthcare field, there are many opportunities and insights that can be gained through the application of artificial intelligence techniques.<\/p>\n

The Paipe project, in partnership with Finep and the Ministry of Science, Technology and Innovation, with resources from FNDCT, seeks to innovate the process analysis sector to make it even more assertive and agile through the use of Machine Learning.<\/p>\n

In this article, we will show the challenge that originated the project, how technology helps, and why AI has so much potential in healthcare.<\/p>\n

What is healthtech and why does AI matter in healthcare?<\/h2>\n

Healthtech is the set of technologies applied to the healthcare sector to make it more efficient, accessible, and safe. Within this field, artificial intelligence is gaining ground precisely because it handles large volumes of data and complex processes well.<\/p>\n

Where AI is most helpful<\/h3>\n

From analyzing documents and processes to forecasting demand for supplies, AI is particularly effective where there is repetition, volume, and a need for data-driven decision-making\u2014exactly the profile of many administrative routines in healthcare.<\/p>\n

The challenge<\/h2>\n

The main challenge is to make the process of analyzing administrative appeals for reimbursement to the SUS (Brazilian Public Health System) automated, agile, and simplified, while maintaining a large database of intelligence to ensure performance in learning and in the responses provided.<\/p>\n

A manual and slow process.<\/h3>\n

These appeals are received every month, and the ANS (National Supplementary Health Agency) needs to evaluate and approve or reject them. Currently, this is done manually by ANS technicians, which makes the process slow given the large number of cases.<\/p>\n

How we help<\/h2>\n

The solution begins with the creation of a legal process analysis platform using a Machine Learning algorithm, developed with the aid of NLP (Natural Language Processing) libraries in Python, aiming to bring agility and simplify the analysis of legal processes.<\/p>\n

The goal is to reduce manual labor.<\/h3>\n

With the implementation of the platform, the focus is on an initial reduction of 60% in the manual workload of technicians. As the artificial intelligence algorithm learns from specific cases, performance tends to grow rapidly and accurately.<\/p>\n

What the platform does<\/h3>\n

The platform will be able to analyze, interpret, and provide a preliminary response to the technicians and, depending on the level of complexity, automatically respond to these challenges.<\/p>\n

Infrastructure prepared for scaling.<\/h3>\n

The stored data will be processed in large volumes (big data), hosted on cloud servers, allowing for scaling of storage, memory, and processing resources when necessary. Furthermore, redundancy and clustering will be implemented for improved performance.<\/p>\n

The role of NLP in the analysis of legal processes.<\/h2>\n

Legal and administrative processes are, in essence, text-based. That's why Natural Language Processing (NLP) is so central to this type of solution.<\/p>\n

From reading to interpretation<\/h3>\n

NLP allows the system to "read" documents, identify the relevant points of each challenge, and classify them\u2014transforming unstructured text into organized information for decision-making.<\/p>\n

Continuous learning<\/h3>\n

The more cases the algorithm processes, the more it learns the patterns of that type of process, gaining accuracy over time. Technology does not replace the technician, but frees them up for cases that truly require human judgment.<\/p>\n

Benefits of intelligent automation in healthcare<\/h2>\n

Applying Machine Learning to process analysis brings benefits that go beyond speed:<\/p>\n