Skip links

Can wearables help you pay lower insurance premium?

Jump To Section

There is no denying that today, there is a flavor of the Internet of Things (IoT) in almost anything and everything we deal with in our day to day lives.

Connected Healthcare: IoT for Wellness

There is no denying that today, there is a flavor of the Internet of Things (IoT) in almost anything and everything we deal with in our day to day lives. This makes more and more data available that makes an average consumers’ life easier and by that virtue, greatly helps companies that are on a constant prowl to build and develop deeper and more personalized solutions/products for their customers.

Gartner has estimated that about 6.4 Billion connected things will be in use by the end of 2016, with close to 5.5 million new things getting connected every day. With significant adoption and incremental inter-connectivity that the world of IoT brings, the opportunity for Connected Healthcare is unparalleled.

This boom in the connected healthcare sector is driven by – Data and Analytics. Thanks to the terabytes of information available digitally, today’s consumers are more aware and want to know everything about their health and have a sense of control over it.

And thus, comes the opportunity for Enterprises and Consumers to ReImagine healthcare for a better and healthier life wherein, connected health is not merely about communication channels between a patient and his/her provider, but is used effectively by the wellness-seekers also. The wave of wearable technology has at both, conscious and sub-conscious levels, made us intrigued about connected healthcare.

But given the opportunity and potential, we aren’t alone as other sectors including pharmaceuticals, healthcare devices, connected device and even insurance companies are constantly endeavoring to make meaningful products and solutions, and by that virtue, a sustainable and scalable business model out of it.

Personalized Premiums: Wearables Transform Insurance

While the healthcare industry has already adopted connected solutions that are used primarily in medical devices, remote patient monitoring, mHealth applications, reimbursement models; today Life Insurance companies are making use of this disruption too.

The genesis of which, stems from a concept that was seen in the field of vehicular insurance – PAYG (Pay As You Go) wherein, the consumer’s longetivity is focused on monitory benefits for those who are healthier just by paying lower premium values.

The present (traditional) model (of insurance companies) calculates the premium by considering factors like age, gender, weight, height, dependent occupation, smoking, risky hobbies and also conduct amedical test. The amount of premium value for the followingyears increase when policyholders claim the insurance or fall under different age brackets (for example: 20-35 years, 35-50 years, 50 years etc).

Thus both insurers and policyholders face problems wherethere are abnormal increases in the insurance premium and the constant risk of inaccuracies in premium values depending on when or how quickly a customer has to be provided with a policy/service.

Arguably, wearables today, are non-HIPAA (Health Insurance Portability and Accountability Act) compliantentities which leads to the rise of issues around data privacy and information security as the data these devices churn-out are personal data that expose a user to threats that go beyond the realms of eHealthcare.

In this regard, a new set of rules and regulations could be introduced for these devices to make them HIPAA compliant and there by mitigating most, if not all, security risks. In the context of data sufficiency and relevance, insurers too face an extremely uphill task of ensuring that the data that comes from the wearables is authentic as it directly impacts the premiums they collect from the user.

The ideal way for Insurance companies is to tie-up with the wearable manufacturers or bring in the BYOD (Build Your Own Device) concept in order to give a more holistic service whilst ensuring gains in the form of ‘fixing most accurate insurance premium’ per user’.

In such a scenario, the threat of drops in the usage of wearables can be reduced because the customers who use wearable devices in conjunction with insurance company are twice as likely to still be engagedwith their devices. This loyalty and backed by the high levels of engagement, help in creating several other scenarios such as:

· Increasing life longevity
· Customized medication
· Predictive analysis of health and hence avoiding any life threatening medical conditions

So how exactly would the use of wearables help in reductions of premium amounts? Quite Simple! They do their standard and usual job of a tracking device that provides real-time data that is tangible, trend-based and predictable.

Adopting such technology, insurers are likely to reduce the size of their claims in the future as they will be able to measure risks more accurately – simply put, the more real-time data that one gets, the better it can be translated to a person’s health. For example, if a person is healthy and maintains a healthy lifestyle, there would incentivized monetary benefits on the premium and conversely, if the health is deteriorating because of an unhealthy lifestyle, the healthy premiums would rise. Not only does contribute to good health, it brings back tangible wealth!

This article first appeared in The Economic Times.

About Author

With over a decade of experience in business enhancement roles in different companies, Kuldeep is a part of the strategic business research team at Altimetrik. His core responsibilities center around market watch, data-driven strategic insights, and initiatives to help the business mitigate risks and generate better results.

Kuldeep is a bit of an adrenaline junkie with a keen interest in the latest developments in the world of business and technology.

Kuldeep Sharma

Kuldeep Sharma

Latest Reads

Subscribe

Suggested Reading

Ready to Unlock Yours Enterprise's Full Potential?

Adaptive Clinical Trial Designs: Modify trials based on interim results for faster identification of effective drugs.Identify effective drugs faster with data analytics and machine learning algorithms to analyze interim trial results and modify.
Real-World Evidence (RWE) Integration: Supplement trial data with real-world insights for drug effectiveness and safety.Supplement trial data with real-world insights for drug effectiveness and safety.
Biomarker Identification and Validation: Validate biomarkers predicting treatment response for targeted therapies.Utilize bioinformatics and computational biology to validate biomarkers predicting treatment response for targeted therapies.
Collaborative Clinical Research Networks: Establish networks for better patient recruitment and data sharing.Leverage cloud-based platforms and collaborative software to establish networks for better patient recruitment and data sharing.
Master Protocols and Basket Trials: Evaluate multiple drugs in one trial for efficient drug development.Implement electronic data capture systems and digital platforms to efficiently manage and evaluate multiple drugs or drug combinations within a single trial, enabling more streamlined drug development
Remote and Decentralized Trials: Embrace virtual trials for broader patient participation.Embrace telemedicine, virtual monitoring, and digital health tools to conduct remote and decentralized trials, allowing patients to participate from home and reducing the need for frequent in-person visits
Patient-Centric Trials: Design trials with patient needs in mind for better recruitment and retention.Develop patient-centric mobile apps and web portals that provide trial information, virtual support groups, and patient-reported outcome tracking to enhance patient engagement, recruitment, and retention
Regulatory Engagement and Expedited Review Pathways: Engage regulators early for faster approvals.Utilize digital communication tools to engage regulatory agencies early in the drug development process, enabling faster feedback and exploration of expedited review pathways for accelerated approvals
Companion Diagnostics Development: Develop diagnostics for targeted recruitment and personalized treatment.Implement bioinformatics and genomics technologies to develop companion diagnostics that can identify patient subpopulations likely to benefit from the drug, aiding in targeted recruitment and personalized treatment
Data Standardization and Interoperability: Ensure seamless data exchange among research sites.Utilize interoperable electronic health record systems and health data standards to ensure seamless data exchange among different research sites, promoting efficient data aggregation and analysis
Use of AI and Predictive Analytics: Apply AI for drug candidate identification and data analysis.Leverage AI algorithms and predictive analytics to analyze large datasets, identify potential drug candidates, optimize trial designs, and predict treatment outcomes, accelerating the drug development process
R&D Investments: Improve the drug or expand indicationsUtilize computational modelling and simulation techniques to accelerate drug discovery and optimize drug development processes