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Uncovering FMLA Abuse – With a Little Help From Big Data
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Pulling a sickie. Faking out. Playing hooky.
Whatever you call it, unwarranted employee absences — particularly those that fall under the category of FMLA (Family and Medical Leave Act) abuse — can be tricky to track and manage.
Just thinking about FMLA abuse is enough to trigger anxiety.
Consider the widely reported statistic that one out of every five employees at the Cook County, Illinois, Sheriff’s office was taking FMLA leave on any given work day. Or think about the December 2012 court ruling that an employer acted lawfully when it terminated an employee after Facebook photos showed her drinking at a local Polish heritage festival when she was on leave due to a serious medical condition.
According to the Society for Human Resource Management, 52 percent of all U.S. employers believe they have granted unfounded FMLA leave. When the situation is chronic, it can seriously dampen employee morale, and also drive up costs associated with replacement workers and lost productivity.
At the same time, employers must ensure they are not denying leave to which employees are legally entitled — or face the risk of a claim.
Here’s how to analyze both risks: FMLA abuse and non-compliance. Follow these steps to determine whether your organization is being to loose or too rigid with the law:
Before you can spot anomalies that suggest FMLA abuse or non-compliance, you must have the right data.
The good news is that most organizations will already have this tracked somewhere, either in their own organization or with a third party. The first step is to understand where your data is and to start to use it.
By looking at absence rates, you can determine your organizational patterns for leave requests for specific work groups, geographies or divisions.
These patterns enable you to spot outliers in the data (i.e. people who take more leave than average or groups that are taking less leave than average).
If you see patterns in the data that suggest potential abuse, it’s important to look at the details before acting. Did the employee submit all the paperwork? Did they seek medical re-certification after their circumstances changed? Did they follow the company’s call-in policy?
Lower-than-average leave numbers can signal that employees are being denied leave to which they are entitled. But they can also suggest that a particular group is highly engaged and effective.
This is why it’s important to delve deeper into workgroups that are taking less leave than average by looking at areas such as engagement score data, productivity data, or leave requests submitted.
You have the right to (periodically) check in on employees who have been granted leave (as long as you aren’t assigning them work, of course.)
Taking a supportive approach to this conversation and explaining the ways in which the organization can support their return to work can have a positive effect, reducing the number of absences taken. And you may find information that indicates they are no longer entitled to time off, based on the FMLA criteria.
On the flip side, what if you suspect a manager’s practices are putting your organization at risk for non-compliance? In this case, it will be important to sit down with the person who is responsible for granting leave requests and :
You can then monitor the data to track results.
By analyzing patterns in your data, you will be in a better position to manage FMLA abuse (and the associated headaches).
You can also take steps to ensure employees who are faced with the challenge of caring for an elderly parent, recovering from a serious injury, or suffering from a debilitating medical condition have what they need: an appropriate amount of time off to deal with the situation.