However, using data and analytics means something entirely different from one employer to the next. For example, consider how you might respond to any of the following questions:

• Are you working with your broker to determine the risks and rewards of taking a larger deductible in a hardening market?
• Are you supervising the claims efforts of your TPA or insurance carrier to make sure they are closing out claims quickly enough?
• Are you self-administered, directly involved in adjudicating claims and looking for benchmarks to target reserves or to evaluate the risk severity of new claims?

No matter how you answer these questions, data can help you to make better decisions. The following are two examples of how Origami Risk clients have used the data they've captured to make decisions.

Example 1: Determining if the changes implemented actually worked

For instance, based on accumulated injury data, a safety professional identified the need to install a relatively expensive non-slip surface in specific work areas at multiple locations. Given the facts that the surface installation involved a significant expenditure and the organization had dozens of similar work areas, the key question was: Will the non-slip surface prevent enough accidents to justify the expenditure?

In this case, the employer tested the concept at one location, hoping to see a steep downward trend line indicating a significant reduction in accidents. Instead, the loss experience in the year following the pilot installation did not show any meaningful improvement. While this was a disappointing result for the flooring solution, the pilot enabled the employer to determine that the installation was ineffective and avoid committing the substantial resources required to install the non-slip flooring at three dozen similar locations.

Example 2: Identifying why claims have increased

With respect to claim severity, most employers generally know which of their operations or facilities tend to have the largest claims. By analyzing loss data, one employer identified a trend of rapid growth in claim severity in the past two years.

During the U.S. economic recession, a healthcare organization saw a significant spike in claims from aggressive patients in its mental health hospitals. Reductions in mental health resources at the local levels led to an increase in patient counts at these facilities. However, during that time the state was imposing a hiring freeze to save money on salary and benefits. Meanwhile, there was a dramatic rise in workers’ compensation claims overall, but an even bigger spike in high-cost claims. By analyzing the number of employees per patient, the organization realized that they needed to hire more workers.

Conclusion

Today, all risk professionals can share an elevated expectation for the role data plays in their workflow. If your organization is a public entity that’s part of a pool or joint insurance fund, the pool administrators are likely utilizing these metrics to manage your risks and losses.

Similarly, if you are collaborating with your insurance agent through the use of spreadsheets, any of these analyses may apply to you. There is no longer a clear dividing line between the largest organizations with RMIS budgets and the rest. Complex systems, like Origami Risk, are powerful time savers because you can leverage analytics reports and dashboards.

However, even if your organization lacks the budget for a RMIS, you may still have the ability to tabulate targeted analytics using simpler tools or by leveraging the tools of your vendors.