Podcast 23 mins
Better Being Series: Understanding Burnout in the WorkplaceTech Innovations and the Future of Health and Benefits
Technological innovations offer business leaders many opportunities to improve the cost and efficacy of employee health and benefits plans.
Key Takeaways
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New technologies are making an impact on all aspects of healthcare — including prevention, diagnostics and treatment.
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Improving health and benefits programs through technology could enhance employee experience and improve organizations’ bottom lines.
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While there are many potential advantages to using new technology in health and benefits plans, cyber security needs to remain of prime importance.
Overview
As technology advances and the needs and preferences of workers evolve, many business leaders are examining how to best harness the latest technological and modeling developments to improve their workplace health and benefits offerings.
Though these developments may bring up as many questions as answers, the potential new technology has for improving the cost and efficacy of health plans could lead to better and more competitive offerings. Better benefits plans can not only lead to a healthier workforce but also help to improve an organization’s employee value proposition — the unique sets of benefits and offerings provided to employees to attract and retain talent — and may also help reduce the stress in an employee’s life, which can lead to burnout.
Doug Melton, global analytics and actuarial leader for Aon Health Solutions, recently sat down with the On Aon podcast to share his thoughts on what leaders should know about the latest technology and health and benefits.
What are some of the new technologies affecting healthcare?
Doug Melton: Let’s start with prevention. Remote devices and smart sensors have been around for a while now, but recent developments in wearable technology are creating many new possibilities for prevention. We’re able to look at things like sleep recovery rates and irregular heartbeats and test them on a daily basis, whereas before, that had to be done once a year during a physical. Big picture: moving beyond annual physical check-ups to the kind of daily care that wearables could facilitate would help identify all sorts of potential issues and lead to their prevention before they become serious enough to require treatment.
Moving beyond annual physical check-ups to the kind of daily care that wearables could facilitate would help identify all sorts of potential issues and lead to their prevention before they become serious enough to require treatment.
As for diagnosis, artificial intelligence (AI) technologies such as natural-language processing and image recognition are starting to be used a lot, especially in MRI and CT scans. These sorts of tools are helpful in reducing human error. For example, one study published out of MIT showed a machine-learning model built for ICUs “predicts with 78% accuracy the likelihood that a sepsis patient will die after 30-days of ICU discharge.” These technologies won’t replace specialists, but most major medical systems are using this type of technology to enhance diagnosing, which has led to fewer errors.
Finally, for treatment, new innovations in data analytics have allowed doctors to use previously rare gene and cell therapies to save patients’ lives. These costly therapies are becoming more feasible due to advanced forms of actuarial forecasting that allow for gaining a better understanding of a treatment’s efficacy from less data. Traditionally, it has been difficult to find the right treatment for rarer conditions because there was not as much data available on them as there was for, say, a generic drug that’s been on the market for 20 years. But now that new methods of actuarial forecasting are available, we don’t need that long of an observation period or that many people to have the same level of confidence.
What’s the difference between new technology such as generative AI and more traditional approaches such as predictive analytics?
Doug Melton: The primary difference is simply that AI can be autonomous and learn on its own, while the predictive analytics we’ve used in the past cannot. Data scientists used to have to go through the processing of building models through human-based iterative learning, instead of having the modeling work on its own.
That being said, traditional predictive analytics have not gone away. They are still being used for forecasting among actuaries. A lot of self-insured health plans and hospitals are using traditional predictive analytics to predict disease progression and to figure out which cohort of individuals they want to engage today versus those whose diseases are not progressing as badly and can stand to have delayed or lessened treatment.
How can organizations keep their data safe while getting the benefits of new technology?
Doug Melton: Cyber security should always be a top priority. Data privacy is something organizations continue to focus on and be transparent about with clients, vendor partners and employees. Having secure private health data to minimize data breaches and safeguards in place for when computational errors occur can go a long way in minimizing cyber risk.
An important method companies can use to protect their data is to use different data stacks and data lakes for transactional business versus for research and innovation. No one wants their innovation data to be mixed up with the enrollment prior-authorization data that was collected on behalf of a client.
Good data hygiene not only protects sensitive data, but it also allows people to get the care that they need and make sure that it is paid for appropriately.
Why do these new technology advancements matter for employers?
Doug Melton: If we take the previous framework or prevention, diagnosis and treatment and put a health and benefits spin on it, we could look at it as open enrollment, selecting the benefits that you need and using those benefits. These new technologies go across that value chain and will help us do what’s called smart enrollment.
Now people have this tech-backed approach that is in almost every part of open enrollment. You give the platform that you’re using a little bit of information about you, coupled with whatever historical data it has, and it will recommend what range of health benefits, voluntary benefits and even some non-insured perk benefits that they think would benefit you the most.
There’s a lot of analysis that’s done today to tell an employer, “Hey, that behavioral health vendor is really working for adolescents, but it’s not working for adults,” which would then prompt them to think about different strategies. They could bring in multiple behavioral health vendors instead of a single one or try to find a new one that suits everyone. An employer benefits from this awareness — they keep their workforce happy through smart enrollment while making cost-effective decisions that help their bottom line.
To find out more about this topic download our whitepaper How Technology Will Transform Employee Benefits in the Next Five Years.
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The information contained herein and the statements expressed are of a general nature and are not intended to address the circumstances of any particular individual or entity. Although we endeavor to provide accurate and timely information and use sources we consider reliable, there can be no guarantee that such information is accurate as of the date it is received or that it will continue to be accurate in the future. No one should act on such information without appropriate professional advice after a thorough examination of the particular situation.
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