Within these walls, researchers are detecting and diagnosing rare diseases using data. They have trained machine learning algorithms to be able to spot complex patterns in the health records.
This allows them to assess the risk of any particular individual having a potentially life threatening condition by passing their health records through the trained algorithms.
It’s hoped that this will result in a cheap and efficient way of screening for rare diseases, including certain forms of cancer, post-stroke complications and even to detect undiagnosed injuries in professional football players.
They’re also able to use these algorithms to predict which patients are at risk of relapse or complications after a hospital visit. Applied before the patients have been discharged, medical staff can arrange for additional care and support for those that are most at risk.
New sources of data, such as electronic health records and patient treatment profiles, combined with advanced techniques like machine learning are heralding a new data-driven age in medicine.
Over the next decade, these trends will revolutionise medicine.They’ll address some of the most fundamental issues in healthcare today, such as shortages in primary care staff and the treatment of complex chronic disorders that are becoming increasingly common.
Data-driven medicine will also feature much more tailoring and personalisation of treatments to individuals.
The implications for protection
Healthcare analytics are addressing the same questions as those we’re interested in from a medical underwriting point of view. Both are about assessing the risks of future medical events occurring.
Life insurance can use these same techniques to underwrite individuals. And just as healthcare analytics is personalising medical solutions, underwriting analytics based on machine learning will allow the personalisation of the underwriting journey. In the very near future it will be possible to tailor underwriting requirements, including questions, to individuals based on applying machine learning to their health data.
The effects of all this on the protection industry are likely to be profound. The tailoring of underwriting to individual customers is likely to result in dynamic application forms and journeys, giving a much-improved customer experience.
New channels will appear and new customers will be encouraged to buy protection by innovative propositions led by this data-driven underwriting. It will also allow providers to offer cover to individuals with conditions that mean they would otherwise struggle to find cover.
Data and analytics are set to transform many other activities in the protection industry, including pricing, customer insight, fraud detection, product design and marketing.
This is an exciting time to be working in analytics in protection. The underlying technology has reached a state of maturity that allows relatively easy adoption. Richer and more comprehensive sources of data are also now available.
Our expectation is that most providers in our industry will be making significant investments in this area over the next few years.