Actionable Data Drives Sound Decisions

Actionable Data
  • The Dawn Of A New Approach

Big. Personal. Integrated. Predictive. Data.

There are as many words to describe the onslaught of available information as there are data points and data sets. In the employee benefits and risk management worlds, our ability to select and integrate meaningful data, provide accurate and actionable analysis and drive desired business results comprise the elements of the perfect benefits equation. Solving for it, however, requires access, experience and sophisticated tools.

When done correctly, plans optimize cost, quality and service to benefit employers and their members alike. Employers face increasingly complex benefits decisions as costs continue to rise and regulatory complexity grows. Without the best analytical talent and tools, an overwhelming amount of data is “nice to have” and not much use in offering benefits that change behavior or drive real results. In other words, simply collecting and reviewing elements is a futile, time consuming exercise with potentially negative results. Think of it as trying to complete an escape room exercise while blindfolded. Surely, you’d bump around and only with the best of luck, complete the task.

Identifying meaningful data points and parlaying data analytics to manage risk, patterns and predictive models can integrate benefits into total rewards and control costs. An integrated program designed with the backbone of sound analytics also becomes an attraction, engagement and retention tool which is paramount to win the war for talent.

  • Past and Present Lay the Groundwork

When establishing integrated benefits solutions for the future, historical, current and cohort data provide the solid base from which accurate predictive analytics can be developed.

For example, with healthcare coverage remaining the single most greatest expense for employers, starting with a common set of elements provides a sound basis to identify where additional analysis can be used to provide meaningful insights and even alert analysts to other elements that may require attention.

Of course, common health plan analytics continue to manage key factors such as:

  • Demographics to identify those covered under an employer’s health plan. This number has decreased over the past several years as spousal surcharges have become more common.
  • Per Employee Per Year (PEPY) the average cost per covered plan participant. Often used as a benchmark against peers, wide variances can suggest the need for deeper analysis.
  • High Cost Claimants (HCCs) identifies the claims that raise total PEPY. Most organizations find that 80% of claims are incurred by 20% of a plan’s members. Best practice identifies beyond HCCs to determine the probability of future HCCs.
  • Claims Expenses vs. Budget, sometimes referred to as a loss ratio, this percentage compares actual claims to budgeted claims. A ratio of 100% or lower indicates claims are as expected or lower (better) than expected. Best practice is 80% – 85%.

Reporting past results illustrates areas that are ripe for improvement. While many brokers provide reporting that includes these areas, they are lagging indicators – simply informing their reader of what has already happened. Analysts need to dig deeper to identify the best NEXT action steps.

Decisions made in a vacuum can be the most costly. Employers who react to the skyrocketing cost of pharmaceuticals by increasing co-payments often ignore the unintended consequence of lower medication adherence. Lower adherence can lead to higher claim expense from chronic claimants. Meaningful data requires a more thoughtful approach whereby analysts have sound cohorts available to guide the employer’s decision process with precision. Source: Unleash Your Data From Silos

Integrating data points beyond those contained within a health plan alone produces the framework to shape, measure and drive the performance of benefits, total rewards and risk management strategies. Including data beyond traditional carrier reporting allows population health analysis. Additional sources can include workers’ compensation carriers, payroll, employee-declared demographics, retirement and HRIS performance management systems.

Consider the impact on prescription coverage cost on employers and employees. Employees suffering from two or more chronic conditions (metabolic and pre-metabolic) may benefit from a wellness program efforts focusing on improving lifestyle choices such as diet and exercise which may occur outside of the Rx plan or the traditional health plan. These changes may also impact absenteeism and over time, productivity measures.

Another example considers Emergency Room usage. For many organizations, data analytics reveals high numbers of Emergency Room visits. The result impacts not just the health plan but can include prescription coverage, and other benefits such as dental or vision coverage, depending on the reason for the utilization.

Further analysis can identify potential reasons that participants choose higher-cost Emergency Room service over lower-cost Primary Care or other clinical settings. It may be as simple as what happened when one company discovered that employees’ work schedules are the same as preferred physicians’ hours. Solutions included flex scheduling at the workplace and the creation of greater awareness of physicians with more accommodating hours. Other, more complex reasons may include a misunderstanding of first dollar health plan coverage and a lack of understanding of 24/7 provider alternatives. Data analytics can get to the root causes in a specific population.

  • The Magic Sauce

An over-arching benefits strategy becomes a usable road map when developed with analytics including past performance, current state and predictive modeling. While numbers, trends and software produce reports that frame strategies, reports alone won’t map actions and develop strategies to change behaviors and create desired outcomes. Human intervention adds the magic sauce that makes analytics come alive.

Trained data analysts should have proven, deep understanding of employee benefits and in many will hold specific insurance licensures and benefits accreditations. This allows the analyst to communicate analytics in understandable and actionable terms and develop sound recommendations. As a member of an employee benefits team, a skilled analyst adds value to both the art and science of benefits strategy, plan design, implementation and employee engagement.

The Emergency Room visit example is useful to illustrate this point as well. Further analysis of the company described previously revealed that employees with chronic conditions were most often visiting the Emergency Room. Had these conditions been identified and “triggered” earlier, employees may have been encouraged and alerted to visit their primary care physicians before their health needs became acute.

  • Six Steps to Success

Change is hard. Integrating data analytics into a total risk management strategy requires time, effort and partnerships with leading professionals who can help uncover the story your company’s data will tell. While the journey may seem daunting, the end results of cost control, employee engagement and desired outcomes are worth it.

InfographicWhile every organization is unique, the process outlined below has helped many gain control of their data and move along the course toward Health and Risk Management.


Six Steps to Success

  • Get Your Data Moving

As the pace of business accelerates and data proliferates, it’s more important than ever to partner with risk management professionals who understand the challenges, complexities and rewards of data-driven decision making for true total rewards development and management.

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To learn more about Oswald’s extensive, proven, sophisticated data methodologies and tools, contact:

Kyle AnthonyKyle Anthony
Human Capital, Benefits

Connect with Kyle on LinkedIn.



Note: This communication is for informational purposes only. Although every reasonable effort is made to present current and accurate information, Oswald makes no guarantees of any kind and cannot be held liable for any outdated or incorrect information. View our communications policy.

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