Are your Leave Specialist KSAOC’s enhanced by your HR Technology? Part III

In part two of this blog series, one KSAOC highlighted was analytical orientation. Decisions should be based on data and data trends and not on gut feelings and notions. An analytical orientation enables you to measure and understand improvements in program effectiveness. In this final segment of how Leave Specialist KSAOC’s are enhanced by your HR Technology, we will focus on how HR Technology can improve your understanding of and can help reduce your disability and workers’ compensation cost and utilization. Ask yourself the following – ***How well am I able to measure my disability and workers’ compensation cost and utilization?*** Both monthly statistics and year-over-year information should be readily available. These statistics should be provided independently from your vendor management to eliminate all sources of potential bias. Accurate measurement is the sound basis for all future steps in the analytical process, so ensure that you’ve taken the best steps. ***Am I easily able to interpret the statistics received?*** You are probably quite adept at understanding incidence, duration and average cost metrics. But what about identification of high-risk claimants and correlations between factors influencing absence? Having a plethora of metrics doesn’t provide value to the Leave Specialist or the organization if the metrics are not understood properly. ***Can I effectively communicate analytic results in such as way that they lead to action and change within the organization or reinforce the programs already underway in the organization?*** If your data indicates that incidence is trending upward and is primarily associated with one particular location, then you have the data you need to approach management at that location with initiatives to curb the upward trend. Appropriate communication should also establish HR’s position at the C-Suite table. Demonstrating the full value of analytics will include a return-on-investment analysis justifying your analytic efforts and actions. So how does HR Technology aid in assisting in the above mentioned Leave Specialist KSAOCs? Selecting an HR Technology tool that provides a user-configurable web portal will open the doors to your data in ways not experienced before. The portal will provide reports such as Claim Incident Reports, Top Condition or Injury Reports and Lost Time Trend Reports. The user will not only have access to this data in a web-hosted or cloud solution, but these types of reports can be based on current data. Reports can also be scheduled to run so they are waiting for your review as soon as you arrive in the office. In addition to a user-configured web portal, the HR technology tool chosen should provide predictive modeling tools allowing leave specialists the ability to access and analyze data.. The types of predictive modeling tools that should be available include tools that provide a High Risk Claimant Analysis. This type of tool should identify not just claimants who have a high risk of future cost, but more importantly those claimants whose expected claim experience has the most opportunity to benefit from early management and early intervention. Additional types of predictive modeling should provide tools which are able to identify relationships between your leave management policies and your lost time trends. For example, *to what extent are employees absent on FMLA also migrating or simultaneously out on paid benefits?* *What trends and patterns of behavior are you able to determine?* Predictive modeling helps identify these trends and isolates areas for improvement in your leave management policies. Finally, and most importantly, effective analytics should lead to change in your organization. What type of change? Change that increases productivity by maximizing the number of employees who are at work through minimizing the effects of absenteeism. If analysis has determined that a high percentage of employees absent on FMLA also have lengthy disability or workers’ compensation claims, then quite likely the interactive process is not working effectively during the early stages of the FMLA absence. Understanding this problem from data analysis and subsequently developing better programs and policies will reduce the burden of absence and demonstrate the effectiveness of your analytical skills and the value of your department to the organization.