Recap of Employer-only Sharing Call: Data Metrics and Management Strategy

On March 8, 2023, Business Group on Health launched the first meeting in a new series of monthly, topic-specific employer-to-employer sharing calls, where over 70 attendees convened to discuss data metrics and management strategies.


March 15, 2023

Traditionally, most employers have turned to their consulting partners or point solution vendors to manage data and pinpoint opportunities to improve and better understand the underlying dynamics affecting the health of employee populations. Rising health care costs and the need to show the value of programs, however, have led some employers to go a step further and consider ways to assume greater ownership over this data, with the goal of gathering more in-depth and actionable insights.

#1 Employers are actively in the process of developing their data strategy and goals by identifying problem areas (e.g., condition or disease state, location-based) and evaluating the effectiveness of existing point solutions.

For most employers on the call, their strategic data management efforts are in the early to mid-stages: When polled and asked to rate their current data management strategy from a scale of 1 to 5—with 5 being the most advanced and 1 the least—a majority of respondents indicated that their data strategy is somewhere in the middle of the road, with a rating of 3. In an effort to improve their data strategy, some employers have built a location-based tracking scorecard that tallies top sites by employee population. This location-based approach may be particularly well-suited for employers with geographically dispersed employee populations. Other approaches include identifying problem areas by looking across the main pillars of well-being to track how increases in leaves related to stress or depression or decreases in contributions to 401(k)s, could predict trends in physical ailments. Employers are also looking at their data by specific condition or disease state (e.g., hemophilia, cancer, diabetes) to assess whether benefits and policies they’ve put into place are having an impact on outcomes and/or affecting total cost of care. This information can help determine and then evaluate communication and point solution strategies.

#2 Leveraging a data warehouse provides an opportunity for employers to enhance their analytical capabilities in the long term and gain a “bigger picture view,” but this undertaking is wrought with challenges, including getting too much unusable data or having problems with data integration.

One of the top challenges noted by employers with a data warehouse in place is the inability to pull all the data together seamlessly from their various point solution platforms/vendor partners. The issue of “overdata,” or getting so much data that it can be less than useful, is also a top challenge. In response to such challenges, employers can consider putting together an analytic plan based on top priorities so that it is clear what issues need the most attention.

Employers are utilizing different vendor partners for analysis and implementation of their data warehouses. It was noted that even though these vendors operate as distinct entities, many will have similar types of underlying datasets. Currently, employers are primarily including in their data warehouses traditional claims data, diagnoses, Family and Medical Leave Act (FMLA) usage, short-term disability (STD) and Americans with Disabilities Act (ADA) leave data, patient outcomes and engagement in well-being programs - to name just a few. A potential upside to utilizing a data warehouse is that it provides some correlational data (e.g., correlation between increased utilization of mental health services and decrease in condition severity) that otherwise may have been missed.

Employers expressed concerns about the validity, timeliness and reliability of the data metrics provided by vendor partners. Furthermore, employers noted an inability for their internal teams to gain detailed access to the data captured by their various point solution vendors. Reportedly, third-party vendors will often claim that this information is part of their "proprietary algorithm” - a narrative employers may be starting to challenge as it acts as a barrier, preventing many benefits professionals from gaining a deeper understanding of what is going on with the health of employee populations.

#3 Employers are seeking access to additional data metrics and resources to better tell a story about health and well-being to their C-Suite, who is increasingly interested in data reporting.

Several employers on the call specified that their C-Suite/CFOs are interested in reports that include a wide array of metrics, leveraging data on everything from flu vaccinations, mental health questionnaires, health assessments, and aggregate costs of care, to assess return on investment (ROI) and the impact of all their benefits offerings. Employers shared they would like to have their warehouses include more data on social determinants of health (SDOH), productivity of specific sites, attendance, real-time data on retention and other engagement/participatory data. Employers also mentioned that they would like to include more data metrics related to engagement and employee experience, but this could prove difficult because aside from a few anecdotal examples, engagement data is often not tracked by existing data warehouses. With the increased interest in data reporting, some companies have an internal employee on the benefits team dedicated to strategic data management efforts, while others have multiple work groups spanning across their organizations or are relying on their external vendor partners to create dashboards.

Looking Ahead: Prioritizing Data Infrastructure

With looming economic pressures and rising health care cost trends on the horizon, more and more employers are looking to their data strategy to substantiate the value of existing benefits offerings/solutions. To truly attain greater analytical capabilities and better predict future trends, it is essential that employers prioritize having a strong data infrastructure and data integrity in place. In addition, employers may consider requiring standardization of data from vendors across their benefits ecosystem. Without such processes in place, employers’ long-term analytical efforts may not reach their full potential.

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