PART 2: Improving Workers Comp Starts with Improving Your Data

Article excerpt

When it comes to workers compensation, leading organizations are beginning to build predictive models focused on the underlying costs that increase claim severity. Although these efforts benefit claims management, several limitations will continue to inhibit the overall effectiveness of these efforts.

First and foremost, the sector must expand the focus from reserving and underwriting to gathering better data. Without better data, and the ability to practically use this data, companies will continue to struggle to identify claims that could benefit from additional oversight throughout the life of the claim.

As currently constituted, most of these systems are able to capture, for example, descriptions of the injury or illness that spurred the claim. But much of this information is vague in nature. Most claims systems capture the primary body part injured, but this does not get to the clinical severity of the injury.

The most common injury type for almost all organizations, for example, is lower back injuries. The description "back injury" is too generic to really target underlying risk factors, but if the system is able to capture the primary diagnosis codes identified through medical billing, companies could better distill the real clinical severity of the injured worker. Better precision could improve the process drastically.

For instance, including medical codes 722.7 (for intervertebral disc disorder with myelopathy) or 846.0 (for a sprained/strained lumbosacral joint) would help those analyzing the information get a clearer picture of the claim. Most professionals experienced with these two diagnoses agree that an injured worker with a disc disorder will most likely have a more complex claim than one with a general strain of the back.

However, even though we know that a disc disorder can be associated with additional risk, not all injured workers with this diagnosis will respond to treatment the same way and often will require different levels of oversight. What if one injured office worker is a 65-year-old male with diabetes who travels 50 miles to work everyday? The claim professional may implement a different strategy than she would for an otherwise-healthy, 30-year-old male who commutes five miles to his office job. A complex or lengthy commute could be a huge barrier, discouraging the injured worker from getting back to work sooner.

Unfortunately, due to the current means of data capture, many of these factors remain hidden. They lie only within the notes of the injured worker's file, which cannot be easily accessed by models, rather than stored in discreet data fields that predictive models can incorporate into the overall risk assessment.

Generally, today's predictive models include the following fields: age, gender, cause of the injury, nature of the injury, work status and job classification. To create better predictive modeling tools, organizations should increase the categories of data they capture, perhaps including: prescribed medications (specifically narcotics), socioeconomic factors (such as education level), psycho-social factors (such as job satisfaction) and distance to the primary work site.

Much of this information is included in notes by the claims handler, but if it is not in its own data field, most predictive models will not be able to find it. A number of predictive modeling tools can utilize the free-form text data that is found in notes, but they are limited in their ability when compared to those that use structured data. …