After a year of planning, Open Enrollment (OE) season is finally here. HR professionals and benefits managers have spent the past twelve months preparing to make the benefits enrollment process not only as smooth as possible, but also offer the right mix of benefits to employees. When it comes to healthcare and employee benefits packages, employers face a number of challenges. They’re balancing the need to rein in costs without compromising their ability to attract, engage and retain top talent – a factor especially important in the current job market.

As companies balance both sides of this, an increasing number are turning to data and analytics for enrollment planning and carefully crafting personalized plans for their employees. But the key to creating a truly efficient, cost-effective healthcare program is embracing data as an asset. Employer decisions supported by actionable, intelligent data captured throughout the entire benefits cycle – before, during and after OE – can drive both savings and high-quality plan design.

The planning phase

To manage continually rising health care costs, employers must evaluate and refine their plan design strategies annually, figuring out what changes need to be made to contain costs. This challenge has grown more complex in recent years as employers fold consumer directed health plans into their strategies and increase their voluntary benefits offerings (the average number of benefits offered to employees through the Benefitfocus platform is 15).

Before reviewing employee election trends from the previous year’s enrollment period or sitting down with a broker to discuss strategy, employers should ask themselves a number of questions. For example: are my employees overspending on prescription drugs? Are claims costs higher than anticipated? Should I raise employee premium contributions?

Answering these questions will help employers identify and set goals around participation rates, contribution amounts and budget priorities for the upcoming open enrollment period. For example, if moving more employees into high deductible health plan (HDHP) options is a goal, benefits managers must first identify how many are currently enrolled and use that to set metrics for the upcoming year. But how does one set a reasonable goal?

Without data or technology tools to streamline health plan design, it can be a time-consuming and labor-intensive process for HR and benefits managers. It can feel more like guesswork than accurate analysis. Sophisticated data analytic tools provide predictive plan modeling and other capabilities to offer a data-driven way for employers to get full insight and achieve more accurate cost forecasting, cost savings and visibility on the financial impact of their decisions. With plan modeling, benefits administrators can review current plans and test different scenarios, without manual configuration, to determine the financial impact of plan design changes on both employers and employees.

By answering key questions and setting cost and plan design goals, employers can be better equipped to partner with an advisor and make data-informed decisions on which benefits will be most effective for an employee population.

The enrollment phase

Employees have access to more benefits options than ever before. So how do they select the best ones? The future lies in data-driven benefits enrollment, which guides employees to the right health care package and personalized benefits recommendations based on their real, personal data.

The good news is employers now have access to the necessary data (demographics, benefit elections, claims and other utilization, compensation and more) to support employees in making sound enrollment decisions. The bad news? The data can be difficult to access and interpret. That’s why benefits administration partners provide tools, services and technologies, along with the necessary expertise, to help employers and their advisors manage and interpret the data necessary to offer benefit options that most closely meet their populations’ needs.

An often-underappreciated aspect of these services is ensuring data security, quality and accuracy. A good benefits technology partner provides access to the right level of data granularity for different users, manages proper security and access control for an employee’s information and provides guidance in choosing the appropriate benefits lineup. As employers collect personal data about employees, they should engage with partners who have proven capability to enable data integration and data exchange from various systems, vendors and partners, and who can deliver benefits data in one consolidated place for analysis. This single view means easier data analysis and better insight into benefits trends, eliminating guesswork for HR and benefits administrators, as well as their advisors, during the planning process.

The importance of this data quality lies in building a foundation of trust and confidence. Employers can’t simply offer creative benefits packages, they must be a supportive resource to employees making the best coverage decisions for themselves and their families. Partnering with a technology partner means an employer can provide more accurate data to employees to foster better-informed decisions, leading to improved well-being for all.

As employers continue to counteract rising costs and shift healthcare costs to employees through consumer directed health plans, there’s greater onus on employees to select and manage their own healthcare. This includes choosing the right medical plans, health savings accounts and voluntary benefits. But industry data points to literacy gaps showing that employees may not be equipped to make the right decisions for their personal situations. To counteract this and better empower their employees, employers are integrating data and tools directly into the enrollment process, delivering education and resources to employees at the point of sale. Similar to the retail world, this enables better-informed consumer decision making at the moment they need it the most.

The employers doing this most successfully are enabling an Amazon-like shopping experience for consumers, equipping them with data-driven insight, personalized plans and the ability to project future coverage needs and costs based on their previous year’s claims data. When shopping for a blender, for example, consumers get curated recommendations based on what else they’ve searched for and product comparisons including things like price and size. Why shouldn’t it be the same for insurance and voluntary beneifts?

Data-driven healthcare plan decision making, in this way, help remove some of the guesswork and allow consumers to make informed decisions based on their needs. Along with understanding available benefits and their per-paycheck costs, insightful tools allow consumers to understand the potential costs associated with their healthcare or other benefits based on their predicted healthcare consumption. This is a significant part of the consumer’s annual cost, yet something often misunderstood at benefit enrollment time, leading to employee dissatisfaction and potential financial hardship.

The post analysis phase

Once OE is finished, benefits managers and administrators can examine the collected data, evaluating the plan selections employees made and the benefits they’ve selected. Data visualization is a helpful tool for employers to splice collected data into easily digestible charts and graphs and answer questions about their employees’ enrollment selections.

And if the benefits team sets pre-determined metrics or goals for plan enrollment, they’re able to measure against these metrics and determine successful plan savings or plan adjustments. Healthcare claims and benefit election records shed light on employee behavior throughout the year and are useful as a benchmark for ongoing measurement and for setting new metrics for the next year’s enrollment period.

Monitoring population health is also crucial as it helps identify cost drivers throughout the entire year. Along with allowing for better forecasting for the next year, this kind of ongoing evaluation of utilization experience offers benefits managers another data-driven way to make better decisions when it comes to plan utilization, member health and key cost drivers.

With this full insight to utilization data, employers and advisors can identify cost trends, especially those associated with individual diseases. Year-round access to data means benefits managers can examine cost management at any time in the Open Enrollment cycle and proactively work to lower costs by implementing changes like care management programs or other solutions, including working directly with healthcare providers or healthcare systems, to target and lower costs and to improve employee wellbeing and productivity. These data trends can then be used to inform plan design for the next year, and cost savings can be invested back into the workforce with benefits or wellness programs designed to target cost drivers, and the cycle begins again.

While these three distinct phases in the process stand out as areas where data can improve decision-making, it’s important employers create an internal culture of data analytics year-round. Placing emphasis and prioritizing evidence-based results in all areas of an organization will make it easier to improve data immersion around health care and benefits.

Phil Bruns is senior vice president and general manager, Benefitfocus. Misty Guinn is director of benefits and wellness, Benefitfocus.

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