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Preventive Technologies, Predictive Analytics and Diabetes


According to the American Diabetes Association, nearly 30 million Americans have diabetes and an additional 86 million have pre-diabetes – a precursor to the full disease. These numbers continue to rise as do the medical costs associated with treatment. In fact, medical costs for people with diabetes are twice as high compared to people without diabetes. Additionally total medical costs, lost work and wages for people diagnosed with the disease has topped $245 million.

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Diabetes (type 2) and other preventable diseases and conditions are responsible for millions of deaths each year in the United States. TThe key to preventing preventable diseases and conditions is for healthcare providers to get ahead of them. In order for this to happen healthcare modesl need to change. The truth is most of today’s healthcare models are reactive. Lets take type 2 diabetes as an example – once a patient starts showing symptoms of type 2 diabetes, the doctor will order the necessary screenings and recommend the appropriate treatment and prescriptions. At that point it is too late, we are in reactive/treatement mode rather than proactive/preventive mode.

It is no secret that the healthcare industry needs to shift its mindset from treatment to prevention and technology will continue to play a larger role in this evolution. Preventive healthcare technologies are being used for disease prevention rather than disease treatment and according to Grand View Research, by 2024, the preventive healthcare technologies and services market will be valued at $432.4 billion globally. If we can connect the dots between the technologies that patient’s are using today with their past medical history, healthcare providers can make more prescriptive decisions, engage in early interventions and begin to slow the diabetes epidemic down.

Obviously this isn’t as easy as it may sound because patients’ health data, especially for people with a condition like diabetes, can be found in a wide variety of places. The key is connecting these data points and translating them into predictive analytics, to generate actionable insights, something cognitive computing can help with. Earlier this year, IBM Watson Health partnered with the American Diabetes Association to apply cognitive computing to the association’s data on diabetes. IBM Watson can translate data into personalized insights that reflect individual risk factors, treatment regimens, and behaviors. Tthe goal of the partnership is to use big data to build cognitive applications for doctors, researchers and patients that can be used for diabetes management and ultimately prevention.

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Kate Tuttle

Kate Tuttle is a senior marketing professional with more than 13 years of marketing experience in both B2B and B2C environments. She has more than 7 years of healthcare industry experience and is passionate about technology and its impact on consumer experience.

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