Medical-Data Analytics:

The Key to Cost Containment

In the 1980s, indemnity made up the majority of workers compensation claims costs. But today, medical payments make up 65 percent of the costs, and that figure continues to grow. The frequency of workers compensation claims has gone down, but the amounts paid to workers who file claims continue to rise. The upshot is that a small percentage of all workers compensation claims is now driving a majority of the costs.

To control costs, carriers need to be able to identify high-risk individuals and figure out how to improve outcomes before “high risk” turns into “high cost.” But that has proven to be a difficult task.

Consider the following:

• Medical claims adjusters handle approximately 250 claims at any given time.
• Many potentially high-cost claims slip through without early detection.
• Even the best adjusters can’t identify the complex patterns hiding among high-cost claims.
• Common pattern-identification methods tend to produce high false positives and high false negatives.

Those are just four of the many factors that have hindered efforts to identify high-risk patients.

The key to controlling costs lies in medical-data analytics. Similar to what carriers have been doing with group health, using predictive modeling to analyze medical data can bring workers comp cost drivers to light. This sophisticated analytic technology can examine hundreds of indicators, such as age, gender, comorbidities, and multiple medications; identify individuals who have high medical need and are at risk for above-average future medical-service utilization; and predict expected losses. Predictive models can quantify the financial implications of a patient’s “illness burden” relative to the population average using an aggregated, empirically validated measure.

Using predictive modeling to analyze medical data can help:

• predict future healthcare costs for individuals or populations
• track illness burden over time and assess trends
• assess trends in utilization of healthcare resources
• profile physician practice patterns
• identify which diseases, members, or populations are driving costs
• identify cases for preventive measures
• measure the value and return on investment of disease- and care-management programs

Predictive modeling offers carriers hard data with which they can be proactive and improve focused service to injured and disabled workers. Predictive models using medical, pharmaceutical, and disability claim data can help both pre- and post-injury. For example, if carriers have the ability to identify individuals at risk for conditions like diabetes, asthma, and congestive heart failure years before their condition escalates into high-cost medical events, they have the opportunity to intervene and possibly help prevent the onset or lessen the magnitude of the condition. Analysis from the models can also help carriers determine areas where safety, education, and injury-prevention programs should be put into place. Taking action at any stage of any disease or illness can help manage escalating costs while greatly improving the quality of life of workers.

In the area of indemnity, modeling can help workers comp carriers determine effective return-to-work, job-duty-modification, and settlement options. By helping to determine which cases to focus resources on, predictive modeling can improve case management, care coordination, medical-utilization management, medical and pharmacy fraud detection, and the speed of claims resolution. Predictive modeling can also help carriers triage claims by flagging more complex claims for more experienced adjusters, case workers, and medical and legal personnel to handle. The faster a carrier can act on a claim, the more cost-effectively the carrier can manage it.

Predictive analytics using medical data has the potential to help in many areas of workers compensation cost containment. By providing a clearer picture of what’s going on today and what carriers can expect in the future, predictive analytics can add a new level of insight into a carrier’s strategic planning.