Benefits & Work-Life Focus


21st Century Analytics Improve Costs and the Employee Benefit Experience

By Eric Helman, Hodges Mace LLC  |  October 2017

The topics of big data and the potential for artificial intelligence to change society as we know it are hard to escape. Nearly all industries are being affected, and the nature of work and how we interact with technology is changing significantly. Why, then, hasn't the world of employee benefits embraced these revolutionary trends? Is there something unique to benefits that makes it immune to the potential these new technologies offer?

The short answer is "no." There is an employee benefits revolution on the horizon, and 21st century analytics is at the core. Through the advancement of true cross-vendor analytics, prescription, engagement and measurement (all of which are brought on by the democratization of big data) will allow employers, brokers and consultants to improve the performance of their benefits plans. Delivering on the dual objectives of providing great benefits while controlling compensation costs will require no less than the application of these latest advancements in technology and process. But first, let's understand why this revolution hasn't happened yet.

Priming for the Future

The application of detailed metrics and analytics in the realm of employee benefits has lagged other industries. Some cite the low transaction environment of employee benefits. Consider: Employee benefits plans typically are evaluated for their performance a few times a year, and are compared to benchmark or market data less frequently. And in smaller environments, this analysis either doesn't exist or is conducted only once a year to support whatever premium increase is requested.

In the middle market, benefits consultants rely on health carriers for any claim analysis. Correspondingly, this information is packaged in the carrier's format, limited to the claims being handled by the carrier, and is unavailable for additional queries specific to potential actions by the employer. In the large-case environment, data analysis frequently is executed by teams of actuaries who take pride in their labor-intensive and deeply analytical work.

Ask any benefits producer who has used these services and you will hear stories of frustration with the lack of speed and responsiveness inherent in these outdated processes. Big data holds the promise to scan huge data sets in a near real-time environment for insights that will affect both the current and future trajectory for a given area. To date, benefits practitioners have not required that type of rigorous analysis.

It's true that large payors have analyzed claim data for years to better manage and assess risk. Unfortunately, the use of this data to improve outcomes has been more muted. Industry insiders confess that, historically, this inaction is in part due to a lack of incentive to reduce costs, with payors who have access to large data sets choosing to use data to both justify rates and manage risk after large claimants are identified. While this situation has improved in the past several years, payor efforts toward disease management using big data still lack widespread adoption.

Gaining a Foothold

Given the recent technological advances in big data, leading benefits practitioners and supporting technology providers are starting to wake up to the many ways metrics and analytics can be applied to employee benefits. But, as is often the case, the technological advances that enable this opportunity are happening outside of benefits. Much of the spade work is being done under the heading of various population health initiatives influenced by the migration toward bundled payment reimbursement. Large health systems, facing penalties for readmission of Medicare patients and confronted with the growing opportunities (and threats) brought on by bundled payments are investing large sums to better understand how patients consume health care and how changes in service delivery can improve both outcomes and profitability.

Data aggregation and data management across dissimilar platforms is becoming easier and holds the potential for scalability outside the very largest groups of individuals. Additionally, these efforts with the large payors have resulted in the development of a new set of tactics for dealing with the identified population health issues. Rather than merely using big data for esoteric analysis, these tactics have the potential to identify specific cohorts of individuals and make targeted recommendations leading to outcomes with a clear return on investment.

At the same time, the pressure on employers to manage costs has spawned a significant amount of creativity by service providers promising to bend the cost curve. From prescription drug optimization to telemedicine, nearly every cost savings idea can be enhanced using the kind of big data analytics now common in the provider community. The niche vendors of these various services use industry statistics and dubious references to illustrate employer cost savings as a portion of their sales process. When it comes time to implementation, their efforts are applied to the broad population rather than being focused on those specific employees and dependents where the greatest gains can be made. Fortunately, this is beginning to change. With the democratization of big data technology, an emerging set of prescriptive analytics solutions are being developed that can provide employers better insight that leads to financially attractive outcomes and full benefits lifecycle transparency.

The 5 Core Elements of Cost Saving Solutions

The integration of analytics and solutions is enabled by a common construction in these disparate solutions that includes five major elements:

  • Qualification (aka problem identification)
  • Justification (aka return on investment projections)
  • Onboarding (aka enrollment)
  • Engagement (aka ongoing messaging)
  • Measurement (aka return on investment analysis).

With big data solutions available to a broader set of employers, these disparate solutions simply need to be layered on top of an analytics platform and then have qualification, justification and measurement components integrated into the data model of the analytics platform. For the first time, when employers review their claims performance in a stewardship meeting, they also will see a variety of actions they can take, prioritized by financial effect. Most importantly, this prioritization is based on the employer's own data.

The newly emerging analytics systems start by combining health and prescription claims, demographic information, benefits enrollment and other personal data compared to clinical norms, national benchmarks and predictive analytics. The information is packaged into a series of dashboards designed to improve the executive suite's understanding of the complex ways this information contributes to overall benefits plan performance. This simplified and standardized presentation is incredibly valuable to brokers and consultants who often struggle to have routine stewardship meetings with many of their clients. But financial reporting and benchmarking without prescriptive suggestions is of little use to small- and medium-sized employers.

To address the full lifecycle of cost saving opportunities, these new systems are integrating the five elements of cost savings solutions within the analytics platform. Preconfigured cards replicate the problem identification and return on investment projections across a variety of proposed solutions. Once an employer decides on a solution, the platform identifies the specific cohort of individuals for targeted enrollment and engagement. Finally, the platform can ingest measurement data to complete dynamic return on investment analysis. The most advanced systems also can be configured to create dynamic triggers that add to the targeted cohort based upon specific claim activity.

One example of this type of prescriptive analytic solution integration can be found in avoidable emergency room visits and telemedicine. Using claims data and knowledge of treatment protocols, the prescriptive analytics solution can highlight the specific number of avoidable ER visits within a given population and predict the financial effect of a concerted effort (through education, plan design and available alternatives) of bending that cost curve. Just as important is the ability to measure the performance of that program (adjusting for population and claims shift) to further monitor the program. This is just one of many programs that can benefit from integration into the analytics platform.

While adoption has been slow, the use of 21st century analytics in employee benefits is beginning to take off. The advancement of true cross-vendor analytics, prescription, engagement and measurement is enabling employers, brokers and consultants to improve the performance of their employee benefits plans like never before. In the PPACA world, with costs rising and the war for talent increasing, employers can no longer afford to manage their employee programs without these kind of insights and prescriptive solutions.

About the Author

Eric Helman is Chief Strategy Officer for Hodges-Mace, where he is responsible for creating, communicating, executing, and sustaining strategic initiatives. He brings a vast background in innovative employee benefits administration and enrollment processes.

Read the October edition of Benefits & Work-Life Focus.

Contents © 2017 WorldatWork. No part of this article may be reproduced, excerpted or redistributed in any form without express written permission from WorldatWork.

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