By Chris Fischer, Advisor, Marsh & McLennan Agency – Houston
In the past 20 years, advances in technology have radically transformed how companies manage employee benefits programs. Paper enrollment forms and spreadsheets have been replaced by mobile apps and Web portals, while cloud-based platforms let employers compile and share benefits data faster and more efficiently. At the same time, these advances make it possible for employers to analyze and leverage data in new ways to improve the design and delivery of their benefit offerings while potentially reducing costs.
Of course, a good data analytics program depends on good data—the more, the better. Key sources for employers include HR, carrier and TPA data related to eligibility status, medical and dental claims, accident policies, cancer programs, HSA contributions, and absenteeism, as well as data from company wellness programs and pharmacy management plans. Although detailed personal medical information is protected by various laws and regulations, including the Health Insurance Portability and Accountability Act (HIPAA) and the Genetic Information Nondiscrimination Act, employers can still identify trends within certain demographic groups or employee populations that can be used to modify plan offerings and incentivize change.
Data analytics previously has been available only to larger employers, who have the staff and the technology to compile data provided in different formats, and a large enough group size to generate reliable results. However, the growing trend of self-funded healthcare plans has made benefits data analysis a viable tool for mid-size firms, as well. Self-funded employers have greater control over their plan design and better access to relevant metrics than those with fully-insured plans, because carriers tend to restrict the amount of data that they provide. Similarly, premiums tend to increase with fully-insured plans even when claims utilization is low, making it difficult to gauge the impact of programs or campaigns designed to mitigate employee health risks or reduce costs. Fortunately, mounting marketplace pressures and increased computing power are pushing health plan providers to deliver more robust benefits data and analysis to fully-insured employers, as well, and experts are bullish on the outlook.
Support from upper management likewise is key to leveraging benefits data analysis, both because of the potential upfront cost of analytic tools and because the leadership can have a profound impact on the corporate culture as changes are introduced. Having a wellness program in place that is customized to the employee population, along with a pharmacy management plan, also can help identify and address current and future health risks among individual employees or employee groups.
Information gleaned from wellness programs is especially useful, because the data not only can be used to shape the design of benefits offerings going forward, but different short-term initiatives can deliver immediate results as employees become more engaged in their health care. Biometric screenings can provide additional employee health data for both descriptive and predictive analysis. Many carriers will include an annual biometric screening free of charge, or an employer can bring in an outside wellness vendor to provide the service. These screenings measure blood pressure, cholesterol levels, BMI and similar health indicators, and can be used to assess an employee’s risk of heart disease, diabetes, or other chronic illness. Combining this information with aggregated data related to prescriptions and claims trends can provide valuable insights to foster positive change and save on annual health care costs.
For example, if data from an employer’s pharmacy program TPA reveal that a large number of employees take Lipitor, a drug commonly prescribed to manage cholesterol, while biometric results indicate that 30 percent of employees are overweight or obese, this can indicate a substantial risk of future medical claims related to a stroke or heart attack. Using this data, HR staff or benefits administrators might launch an internal campaign designed to help employees lose weight; for example, by challenging employees to walk a minimum of 10,000 steps a day for eight weeks and offering an incentive to everyone who completes the program. (Similarly, companies may choose to penalize employees who opt not to participate in wellness initiatives, for example by charging a higher premium rate.)
Looking to the Future
This type of predictive analysis can empower businesses to help employees make positive changes related to their health and potentially reduce claims costs substantially over the long term. By identifying workers who in 2-5 years are at risk of developing a critical illness, like diabetes or heart disease, employers can take proactive steps to change future outcomes, such as providing guidance from wellness counselors and reducing the cost of medications.
Marsh & McLennan Agency offers self-insured employers several tools that can facilitate data analysis and create and maintain a plan that meets the employer’s unique objectives. For example, BST lets employers gather and house all their employee health and benefits data from different sources in a single database, so they can do a deep-dive of the various components that can impact the company’s benefits plan. BST’s proprietary algorithms and AI capabilities create millions of plan scenarios and analyze the employer’s specific population data to generate actionable recommendations for minimizing costs and achieving plan objectives. Similarly, MMA Rx Solutions combines robust analytics with pharmacy contract expertise and employee engagement tools to help employers understand their pharmacy options and maximize bottom line savings.
In short, when done right, data analytics can empower employers to design and maintain plans that have a positive impact on employees’ health and wellness, while also improving recruiting and retention rates and reducing healthcare costs. As carriers offer more sophisticated reporting tools, implementation times for data analytics will continue to decrease, and data collection will become more accessible even for smaller companies. Leveraging the power of big data now can give organizations an advantage over the competition and help them protect their earnings and, more importantly, their people.
This document is not intended to be taken as advice regarding any individual situation and should not be relied upon as such. Marsh & McLennan Agency LLC shall have no obligation to update this publication and shall have no liability to you or any other party arising out of this publication or any matter contained herein. Any statements concerning actuarial, tax, accounting or legal matters are based solely on our experience as consultants and are not to be relied upon as actuarial, accounting, tax or legal advice, for which you should consult your own professional advisors. Any modeling analytics or projections are subject to inherent uncertainty and the analysis could be materially affective if any underlying assumptions, conditions, information or factors are inaccurate or incomplete or should change.
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