We live and work in the age of “data analytics.” With data available at every turn, we thought it would be helpful to take a look at what data analytics is and how it can help claims and utilization review professionals do their jobs better.
Demographic analytics include statistical data regarding geographic areas (region, state, etc.), which can be compared. As an example, data may show that California has a higher cost per claim than found in a neighboring state, such as Oregon. Demographics also encompass information about claimants, such as age, gender and ethnicity and can be compared to the length of time a claim has been open.
Injury analytics are details that generally outline the length of treatment for specific injuries. The data can be classified by ICD9 (diagnosis) codes, body parts and treatments, and may also contain relevance to some of the demographics. Comparing a particular type of injury (e.g. lumbar strain/sprain) in age groups or within specific geographical areas may provide insight into disability management for those segments.
Return-to-work analytics are statistics centered on the expectation that injured claimants will return to work and how various factors may impede or promote return-to-work. Some considerations include co-morbidities, such as obesity, diabetes, prior injuries, etc.
Pharmacy analytics provide an understanding of the factors related to medications. With opioid usage running rampant in the US, this field of analytics has become even more important. Over the past year, numerous articles and studies have focused on the overutilization of pharmaceuticals in workers’ comp (as well as in general). We know there are mitigating factors associated with dispensing drugs. Physician dispensing and weaning protocols are major concerns too often overlooked or ignored. The data on this topic is staggering. Many legislative bills have been created to address these factors.
How can you use data analytics in your day-to-day functions?
Many current studies combine all or some of the above types of data to present detailed metrics to the industry. Recent studies have shown that pharmaceutical costs have dropped in states that have implemented formularies or where physician dispensing has been regulated or outlawed. The data compiled from many of these studies is now being incorporated within evidence-based medicine. Studies indicate that some drugs should not be first line use drugs, and the commercial guidelines support that and allow for the utilization review process to address these concerns for the health and welfare of the patients.
Knowing that a particular population (whether via demographic or injury analytics) has a higher use of a service, drug or procedure can alert the adjuster or other claims professional to more closely monitor a claim with the same or similar conditions. Claims can be flagged based on analytical profiles to ensure they are monitored more closely. In some instances, case managers may be assigned earlier in the claim to remove obstacles to returning to work in accordance with the recommended timeframes.
Educating policyholders and claimants about the evidence supporting return-to-work expectations can be a powerful tool. Policyholders with safety programs are interested in learning the facts surrounding injuries particular to their line of work.
While ‘data analytics’ sounds like a dry or boring topic to many, it can present powerful messages and opportunities to raise red flags to claims handlers. This can be particularly helpful to adjusters as they plan claim reserves and for utilization review providers as they manage treatment decisions and monitor outcomes.