How AI can help save billions in the healthcare sector

-

All about Artificial intelligence

Artificial intelligence has already entered practically all fields of work, including healthcare. It is not uncommon to see hospitals using AI to carry out screenings or even doctors taking advantage of the technology to speed up a diagnosis, improve patient care or help with revolutionary research in the sector.

AI has also proven to be advantageous for companies managing healthcare facilities and health plans, which have saved billions with little investment in technology.

Read more:

AI in healthcare

Help diagnose cancer, speed up data analysis for medical research or even facilitate screening in a busy hospital. These are all possibilities for AI.

According to Michel Goya, CEO of hospital management and logistics company OPME Log, an investment of around R$500,000 can guarantee patient data management, for example. He mentions the technology’s ability to analyze data related to gender, age, previous illnesses, whether a surgery is that person’s first or a reoperation, and more.

Another example is the protection of the company itself. According to the Brazilian Association of Health Plans, health insurers paid more than R$7 billion in fraudulent reimbursements between 2019 and 2022.

Then, an unnamed insurer used AI to identify possible fraud in claims for medical services and saved R$4.2 million in just five.

Image: shutterstock/PaO_STUDIO

Health sector qualification

Goya believes that the advances are a significant milestone in modern medicine, emerging as a fundamental ally for doctors and other healthcare professionals.

He mentions that, in addition to the mere investment in AI, advances are also needed in the usability of tools and technology.

He mentions specific courses on AI application that provide this mastery and can increase a company’s results.


The article is in Portuguese

Tags: save billions healthcare sector

-

-

NEXT How cannabidiol went from a wave of products to calm stressed consumers to stagnation