In a world of future telling and crystal balls, the idea of predictive diagnosis is a simple one. We would simply look into the future, see what it held, and make the necessary adjustments. In reality, however, what’s to come is far less telling. Rather than having the answers given to us, we have to look into the given data, and use it as a tool.
The information itself – known as big data – comes from healthcare professionals from all over. Doctors take notes, see patient information, and then it’s combined into a whirlwind of numbers and facts. Then by analyzing those same numbers, patterns begin to take place, which can then be used for an educated guess into the future.
Today, those using these data analyzing techniques are known as early adopters. These early adopters compile and configure to help both patients and doctors. For instance, with data, doctors can see what percentage of the population has a certain disease, chronic sickness, and what symptoms they had during the process. For instance, doctors can look at recent census data and see that each physician averages 511 hyperlipidemia patients and only 145 diabetes patients. Therefore, statistically, it’s more logical for doctors to study hyperlipidemia treatment options, check for those symptoms, and have accompanying literature in stock.
Likewise, doctors can see what percentage of patients experience medicine reactions and treatment success, and base their prescriptions accordingly.
Additionally, data can be used to predict potential epidemics; patients can be informed of their susceptibility based on visit dates and their medical past. Doctors can also see which illnesses patients are more likely to catch, based on growing stats vs. pre-existing conditions. With this knowledge, preventative measures can be taken and patients, with their doctors’ help, can be better informed as to how to improve their health.
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