March 23, 2021
The ability to accurately diagnose, predict patterns and trajectories of diseases in patients is one of the most important aspects of effective healthcare. The early detection of symptoms can help health professionals to administer the correct treatment, take preventive measures and assess the overall condition of the affected person.
With the growing use of AI and predictive analytics in the medical field, clinicians have been able to develop and test intelligent tools that can help with the swift identification of illnesses by learning patient information, medical records and real time images. The given data is then correlated with the system’s knowledge of medicine and generates optimal results and recommendations, optimizing the work of caregivers.
Although these technologies continue to be in the beginning stages of development for the most part, some models are already being used in facilities for tasks such as detecting early signs of patient deterioration, delivering preemptive care for at-risk individuals in their homes and identifying future equipment maintenance needs.
Aside from its uses in preventive medicine, AI and predictive analytics can improve the way healthcare centers manage their internal operations, forecast patient usage patterns, allocate resources and ensure the safety of confidential data.
These tools can also aid with the prediction of outbreaks in order to plan ahead and warn communities to avoid further spreading. A practical example of this is the case of BlueDot, an AI platform that focuses on infectious diseases, that managed to spot the first major cluster of COVID-19 in Wuhan, China on December 30, 2020. This was nine days before the World Health Organization gave the first official warning of the emerging virus.
All of these advantages lead to long term upgrades such as cost reductions for medical facilities, an increase in the quality of life of the community and in the overall health of individuals, which in turn creates a positive impact in terms of population statistics.
One of the main misunderstandings surrounding predictive analytics is that these innovative tools will replace the need of human healthcare professionals in hospitals, due to their accuracy for detection that is not limited by factors such as time, energy and power.
This, however, is a false statement. The handling of predictive algorithms should always be done by licensed experts who are knowledgeable in the medicine field and have experience carrying out caregiving duties. After all, the human touch of healthcare should never be supplanted by technology.
The role of AI in medicine remains as a support tool to lessen the strain on clinicians and the health system and assist them in their labor of saving lives. The continuous growth of new technologic alternatives aims to build a future where professionals can develop their own skills with the help of novel platforms.
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