By Rogério Pires, Director of Healthcare at TOTVS
Artificial intelligence is not something new. There have been studies on the technology since the 1950s. But it was only in the last 10 years that it started to take off, thanks to the growth of the internet, the computational capacity of machines and the emergence of big data. Proof of this is that in January of last year the term was not even among the 100 most searched on the website of Gartner, a market analysis consultancy. Today, it ranks 7th and is one of five investment priorities for more than 30% CIOs.
In the Health sector, the technology already presents excellent results and can be used both in the diagnostic part – the best known to date – and in the management of the institution. With it, it is possible to avoid glosses and fraud at operators, reduce the risk of contamination in hospitals, optimize the allocation of beds and even improve patient care.
There is no doubt about the benefit of the technology's applicability in the diagnosis or support of medical decisions, as in the identification of cancerous tumors already carried out in the United States. However, artificial intelligence can – and should – also be used to improve processes in institutions in the sector, directly impacting the care provided to patients.
In operators, for example, it can help to identify fraud in advance, avoiding losses. As the authorization request is performed manually, most of the time, performing unnecessary or more expensive procedures than necessary go unnoticed. Based on previous behaviors, the technology will be able to identify them before they happen and deny the request or suggest a more appropriate treatment.
AI also contributes to better control and reduction of nosocomial infections. Currently, the process of releasing and sterilizing beds has a manual check, which is often left out. With technology, this checklist will be automated and will show professionals more clearly what is missing, in addition to indicating how predisposed the institution is to certain infections.
The technology also allows for a more assertive forecast of cancellations and on the show of patients in scheduled procedures, such as appointments or exams. To provide these insights to the business, artificial intelligence analyzes and correlates hospital data, such as the schedule and profile of users, and makes predictions based on history. The impact of this change does not only impact the optimization of care, but also the entire operation, since the appointment is only the institution's gateway and the non-attendance of a patient affects its entire progress - from the allocation of equipment and professionals to the use of hospital or surgical beds.
This, by the way, is another issue in the health sector that deserves attention and can be improved with the adoption of artificial intelligence. The technology allows for a deeper assessment of the institution's data, such as length of stay, illnesses and patient discharge forecast. Today, this entire process is manual, which makes it difficult to predict how many beds will be vacant each day, and computerization is the premise to resolve this issue. It will allow for more accurate planning based on bed capacity and better knowledge of the average length of stay for each patient or disease profile.
Faced with so many possibilities, it is safe to say that this technology will impact the entire chain and not just the diagnostic stage. This contribution will be essential for better patient care, a lower rate of hospital infections and greater profitability for institutions, whether hospitals, clinics or health plan operators.
In the Health sector, the technology already presents excellent results and can be used both in the diagnostic part – the best known to date – and in the management of the institution. With it, it is possible to avoid glosses and fraud at operators, reduce the risk of contamination in hospitals, optimize the allocation of beds and even improve patient care.
There is no doubt about the benefit of the technology's applicability in the diagnosis or support of medical decisions, as in the identification of cancerous tumors already carried out in the United States. However, artificial intelligence can – and should – also be used to improve processes in institutions in the sector, directly impacting the care provided to patients.
In operators, for example, it can help to identify fraud in advance, avoiding losses. As the authorization request is performed manually, most of the time, performing unnecessary or more expensive procedures than necessary go unnoticed. Based on previous behaviors, the technology will be able to identify them before they happen and deny the request or suggest a more appropriate treatment.
AI also contributes to better control and reduction of nosocomial infections. Currently, the process of releasing and sterilizing beds has a manual check, which is often left out. With technology, this checklist will be automated and will show professionals more clearly what is missing, in addition to indicating how predisposed the institution is to certain infections.
The technology also allows for a more assertive forecast of cancellations and on the show of patients in scheduled procedures, such as appointments or exams. To provide these insights to the business, artificial intelligence analyzes and correlates hospital data, such as the schedule and profile of users, and makes predictions based on history. The impact of this change does not only impact the optimization of care, but also the entire operation, since the appointment is only the institution's gateway and the non-attendance of a patient affects its entire progress - from the allocation of equipment and professionals to the use of hospital or surgical beds.
This, by the way, is another issue in the health sector that deserves attention and can be improved with the adoption of artificial intelligence. The technology allows for a deeper assessment of the institution's data, such as length of stay, illnesses and patient discharge forecast. Today, this entire process is manual, which makes it difficult to predict how many beds will be vacant each day, and computerization is the premise to resolve this issue. It will allow for more accurate planning based on bed capacity and better knowledge of the average length of stay for each patient or disease profile.
Faced with so many possibilities, it is safe to say that this technology will impact the entire chain and not just the diagnostic stage. This contribution will be essential for better patient care, a lower rate of hospital infections and greater profitability for institutions, whether hospitals, clinics or health plan operators.
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