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To make better decisions about our care, doctors must weigh not only our DNA makeup, but also incorporate a growing set of health data that is generated and controlled by patients. The benefits of such predictive analytics are the same as other categories of digital health: better care and lower costs. Basically, predictive analytics is the process of learning from historical data to make predictions about any future occurrences. In healthcare, this means that the harvesting of big data and identification of valuable metrics will empower patients to make the best possible health decisions for themselves, tailored specifically to their needs. This notion is not new: most of traditional medicine and health care today already operate with the use of “predictive analytics,” though it is driven mainly by physicians’ rather than computer minds. The goal in expanding the use of predictive analytics is to the widen data set beyond the physician’s experiences so that individual patients can make better decisions when it comes to their treatment options.


Current major categories for predictive analytics are symptom calculators and genetic screenings. Symptom calculators are known as “recommendation search engines” of health care, helping millions of consumers diagnose themselves. Genetic screenings have the ability impact many areas of health. For instance, it can calculate the inherent risks to the mother before a child is born, allowing decisive action to be taken by key stakeholders. Additionally, in oncology about one-half of cancer drugs in 2014 were targeted therapies, a 35% increase over 10 years, indicating both a surge of personalized medicines reaching the marketplace and also the dominance of oncology in personalized medicine. Other applications include disease prevention, population health management, and treatment selection.


The overabundance of data and the widespread availability of these tools has catalyzed predictive analytics in health care. Investors certainly believe in the promise, pouring $1.9B into companies that purport to use predictive analytics since 2011, 4.5x more volume than then. Funded companies have been mostly focused on providers, but new trends are taking shape that are more tailored to a patient-centric experience. New data streams, both patient-generated (monitoring data from patches and wearables) and patient-reported (meal and mood logging), are increasingly being used for analytics.


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