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How Predictive Analytics Can Improve Healthcare?

Predictive analysis, which makes it possible to make assumptions about future events, is not solely for marketing purposes. Large-scale data collection allows analytics to make breakthroughs in many areas, including the applications of predictive analysis for healthcare industry: Better understanding of patients in order to provide them with the best treatment, improving the efficiency and speed of care, anticipating future disorders…Big Data and all predictive analysis technologies can help optimize the management of our health.

Better identify target populations

A comprehensive health plan, an influenza treatment strategy, or the deployment of material solutions for specific people, requires a good knowledge of the target audience. Providing care and counseling to those most in need by analyzing multiple sources of information is now possible.

Between the official data provided by the patients, the shared personal data as well as the consolidated health data, it became easier to conduct a health risk assessment more precisely. It is possible to observe what motivates people and how to change their behaviour when facing health risks, for example by measuring the effects of screening campaigns or requests for information.

As in marketing, it is possible to define test groups, and to carry out real experiments, to find who needs help. Large savings are possible by better targeting people who need to be screened for a type of cancer, or by finding an axis of communication that manages to pass the brakes of a certain age group.

The data allow conducting real A/B tests, as in traditional marketing, but with here as target audience, patients. Or by finding a communication axis that manages to pass the brakes of a certain age classification.

Example with the prevention of the risk of diabetes

Many healthcare companies are working to find diabetes treatments, but the prevention component is also essential. Indeed, intervening upstream among patients in risk groups can reduce the number of people affected. This is when predictive data analysis comes into play, and helps healthcare professionals.

A group of American researchers has developed a statistical model to help prevent diabetes, using a lot of data collected from 3,000 people. The idea, giving patients a percentage of risk of developing diabetes, is to recommend actions to them accordingly. The aim is thus to avoid inflicting unnecessary treatment on people who have little risk of developing diabetes.

“We believe this approach should be widely used,” said David M Kent, Director of the Predictive Analytics and Comparative Effectiveness (PACE) Center at Tufts Medical Center. “How many patients receive treatment unnecessarily, while the effect is limited, and that some people find it difficult to undergo these treatments and suffer? If these types of analyzes were systematically integrated, we would have a clearer understanding of the risk “.

The Internet of objects, and the connected objects of health that multiply, could well be this bridge that remains to be established between the predictive analysis and our body … which keeps its share of mysteries. But with more data communicated, sometimes in real-time, our health will greatly be improved…

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