Prospects for fitness bands, smartwatches and other biosensing monitors have caught the attention of insurance companies. Increasingly, health insurance providers are building programs around biosensors to lower premiums for healthy behaviors. As well, life insurance providers are now looking to insight from wearables data to help them make more informed policy decisions, which would serve to lower premiums for healthy people – and to help steer higher-risk candidates down a better path.
A growing number of insurers are looking to South African startup LifeQ to help develop such sensor-to-cloud programs. Analog Devices is an investor in LifeQ. LifeQ is an attractive partner for such programs, due to its broad suite of metrics to assess heart health, sleep quality and stress, as well as a health information platform to manage all the data.
“This is an exciting time in biosensing, as we’re finally starting to realize the great potential for healthcare providers, insurance carriers – and all of us, of course – to make better decisions,” said Riaan Conradie, President and Co-founder of LifeQ (pictured right). “It’s a win-win, because it eases the burden on our healthcare system and saves insurers money. And in the end, we’ll all be healthier, too.”
LifeQ recently concluded a pilot program with an insurer with the potential to turn the life insurance business on its head. Together, they developed inputs for the insurer’s risk-stratification systems that are used to set premiums.
You might call it a wrist-stratification system. Because the new inputs into the insurer’s new model were derived from insight generated from fitness band data. As part of the pilot, 1,000 test subjects wore Garmin Vivosmart HR bands modified with LifeQ metrics for three months. The sensor complex inside the Vivosmart HR bands are built around Analog Devices’ components monitoring platform, which the company unveiled more than two years ago.
Though it’s vitally important to the success of life insurance companies, the risk stratification process has remained largely static for decades. Insurers typically make premium decisions for policies that can span 10 years or 15 years armed with little more than a questionnaire and a physical. So there is plenty of room to improve.
Indeed, the commercial program hasn’t yet been announced. But the buzz surrounding the pilot – and resulting enhancements to the insurer’s risk-stratification process – is already catching the attention of other insurance providers. We expect more insurers to build wearables-based programs over the next year.
Fit the easy to measure metrics (core temp, heart parameters, skin impedances, activity monitoring accelerometers features) with perceived energy levels gathered by questioning. Using the recently acquired Ramen scattering scanner correlate food eaten with the now predicted perceived energy. Identify patterns and report food types that correlate to less perceived energy and optimum eating patterns.
The link between food and how active you feel is well documented and advertised (i.e. the market interest already exists), there would be a definite value in data sets and fitted models, for example once trained the model would quickly identify what foods you should be eating based on a small data set because it has already identified the trends. Actually the more Machiavellian amongst us would use a financial model that used this data set to disrupt the food shopping market however I would never suggest such a thing.