Teens and Technology and How It Benefits Society
Currently, Australia’s and other countries’ health care models implemented after World War 2 are treating their populations with a ‘reactive’ medical system.
This is where a patient feels sick and the doctor then reacts to the symptoms and behaviours based on the given clinical information, blood tests or medical imaging in order to ascertain a diagnosis and then treatment (Wise, MacIntosh, Rajakulendran, & Khayat, 2016).
A reactive medical system is flawed resulting in a human and financial cost as it treats problems as they arise rather than preventing the complications from manifesting.
I propose that engineering and science can be used in ‘predictive’ medicine to minimise the adverse complications of autoimmune and viral diseases.
This will create a health system model that improves the prevention techniques of autoimmune and viral diseases rather than addressing them when they occur. I was diagnosed with Type 1 diabetes when I was 2 years old. My blood glucose was tested, and I was diagnosed before being admitted into intensive care as I demonstrated diabetic symptoms.
Unfortunately, one-third of children diagnosed with diabetes are admitted to intensive care with Diabetic Ketoacidosis or in a coma (US National Library of Medicine National Institutes of Health, 2016) (Rashed, 2011) (Cameron, Kilov, & Audehm, 2016).
I passionately believe with a predictive medicine model using engineering and science, it would decrease intensive care admissions and save lives.
Engineering could assist the predictive healthcare model by supplying new technologies such as artificial intelligence to monitor body systems and enhance people’s own health management.
In the Type 1 diabetes community, urine samples could be monitored via a sensor in the toilet and warn potential patients of higher than normal glucose readings and ketones. Toothbrushes could indicate antibody or bacterial levels and predict viral infections preventing spread of the disease.
This information could be correlated and produce a patient health report via mobile phone with recommendations such as to see a doctor.
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Eric Dishman stated in a TEDMED that phones are revolutionary to the healthcare system and could use algorithms to detect behavioural cues (Dishman, 2009).