Organizations across the globe realize that healthy employees with a strong sense of wellbeing are more engaged, loyal, and productive. At the same time, in a cost-pressured environment, employers are taking a cautious approach to managing their benefit spends. Many companies have not yet fully embraced a powerful tool that can help them use their resources more effectively.
Incredible advances are happening in machine learning. Artificial intelligence (AI) can help organizations interpret large volumes of data and provide them with meaningful and actionable insights about their employees. In turn, this data can inform and improve the design of health and wellbeing strategies.
As employers look to address key employee health risks, the value of these deep insights cannot be underestimated.
Many organizations struggle to use data effectively and consistently. Fewer than two in ten companies globally use their data insights comprehensively to inform decision-making and understand the impact to their organization of their health and wellbeing programs, our 2024 Wellbeing Diagnostic survey data shows.
Some organizations are using benefit benchmarking data effectively to ensure their package remains competitive in their industry or sector. But very few employers leverage employee insights or health risk data to create a complete profile of their organizational needs (Figure 1).
Source: 2024 Wellbeing Diagnostic Survey, Western Europe
There are significant volumes of employee health and wellbeing data available to employers. It’s provided in different formats, disconnected, and heavily anonymized. This makes it incredibly hard to interpret, and to understand the credibility and relevance of the data being analyzed. To be effective, this must be a continuous cycle of listening, learning, deploying, and revisiting. It isn't just a ‘once and done activity’.
The art, or challenge, is in making your organizational data work harder and smarter for the benefit of both the employer and the employee. That is where the power of machine learning and AI comes into its own. It helps employers to pull together data sets and to make sense of them in an organizational context.
Globally, the prevalence of non-communicable, or long-term, diseases (NCDs) is rising rapidly. While many genetic factors such as ethnicity play a part in disease risk, modifiable lifestyle factors are having a far greater impact on population health.
Globally, the World Health Organization estimate that seven in every ten deaths from NCDs are linked to modifiable risk factors such as smoking, alcohol, diet, excess weight and low activity levels (Figure 2). Many state healthcare systems focus on acute disease management and widescale screening programs, coupled with awareness campaigns, to manage long-term health conditions and health risks. They don't fully focus on preventative measures to support health management and reduce or mitigate future risks.
Through employee benefits programs such as healthcare plans (designed to address short-term, acute, health conditions in many countries), disability and death-in-service products, organizations are increasingly spending their benefits budgets on what are largely preventable costs of employee ill-health.
And this is why leveraging data is so powerful and important for employers. Understanding the specific health risks and demographics of the organization enables an employer to effectively target benefit programs and deliver improved financial and human capital outcomes. Our Wellbeing Diagnostic research indicates that organizations with highly effective wellbeing strategies can expect human capital and financial results that are over two times higher than organizations with less effective approaches.
Lack of consistency, quality, and complexity are three of the biggest challenges facing organizations when working with employee data.
Regardless of their size, employers struggle to effectively connect the various data sources available to them, even if one vendor provides multiple products and solutions for their business. Clearly, data privacy must be maintained but vendors have to do more in this space to help employers access the insights they need to inform and create effective health and wellbeing interventions.
Even when an organization does create an integrated view of specific population health risks and trends, this is often done as a one-off project. Many employers are not measuring human capital and financial outcomes of their interventions to understand the value to their organisation and to their employees and are not able to optimize and evolve their programs to address the changing health risks and needs within the organization.
The issue of resourcing and ownership is a key barrier to success for organizations in this space. And this is where effective use of machine learning and AI could be invaluable. Creating real-time, interactive reporting that manipulates and integrates data from a variety of sources enables organizations to understand health risks and target interventions. Most importantly, an employer can then benchmark the impact and any necessary changes to its current strategy and approach.
Currently, the health and employee experience technology markets are still evolving and most do not yet leverage AI and machine learning to the same extent other consumer platforms like Amazon and Netflix do. As this area evolves, employers will be able to use technology to integrate their wellbeing program vendors, deliver a first-class user experience and leverage outcomes and data to inform and drive their strategy.
Many organizations are facing challenges with rising employee benefit costs, driven by increasing claims costs and growing use of private health services as state health systems struggle to meet demand. Being smart about how benefit programs are designed and delivered to maximize efficiencies and effectiveness has never been more critical .
It is possible to optimize employee benefit spend, and tactical use of data is one way that organizations can do this effectively. Undertaking a benefits optimization assessment benefits optimization assessment, alongside demographic benchmarking and health data analytics will ensure that you are able to focus your benefits budget to deliver results for your organization when and where it matters – and show the return from investment (Figure 2).
Our 2024 Global Benefit Attitudes and Global Wellbeing Diagnostic surveys highlight the impact that understanding and leveraging data insights can have on employee health and wellbeing for an organization.
Highly effective organizations are focused on using technology to personalize their employee experience (EX) and deliver a best-in-class benefits experience for employees. They are also connecting their strategy and benefit programs more effectively to under-served and key employee populations, using data insights to inform and drive their decision-making (Figure 3).
And the results speak for themselves. Where employees understand their benefit programs and feel connected to the organization’s culture of wellbeing, they are between two and six times more likely to be taking proactive steps to improve their health, be more engaged and productive, and have lower rates of absence (Figure 4).
Source: 2024 Global Benefits Attitudes Survey
Contact us to understand how we can help you to use data analytics, machine learning and clinical insights to drive an impactful culture of wellbeing within your organization and optimize your employee benefit program.
