From all the industry buzz about predictive analytics, you would think everyone in workers’ compensation is using it. Unfortunately, that’s not the case. A recent CorVel survey found that 40% of respondents do not currently use any predictive analytics capabilities, and one out of three are not using data to measure and improve claim severity.
What’s the reason behind this gap? The answer: predictive analytics is complicated. Companies that don’t have the right partner – with the right expertise, technology, and connectivity – find themselves at a significant disadvantage.
The Value of Predictive Analytics
CorVel has taken predictive analytics to a new level by bringing together streams of all available data into our integrated claims system, CareMC Edge®, and leveraging industry expertise to identify the overall risk level of each claim. Going a step further, actionable solutions are provided for individual claims based on this analysis.
We’re able to deliver this depth of insight because — unlike many firms that use a variety of vendors for different functions that create disparate and separate data streams — CorVel’s claims system brings this vital information together.
One differentiating capability that significantly impacts CorVel’s customers is the new Claim Risk Score interface in CareMC Edge. The system reviews all data elements of a claim and assesses its level of risk — low, medium, or high – to calculate its risk score. The scoring process considers a diverse set of variables such as return-to-work estimates, physical requirements of the job, the patient’s pain level, comorbidities, psychosocial indicators, claimant medical compliance, and the claim history.
As these facts are analyzed, actionable recommendations are identified in real-time and delivered via the CareMC Edge application for the adjuster to proactively manage the claim for the best possible outcomes. The risk score and alerts begin firing as early as day one of the claim. The score and recommended action plan continually recalculate as new information becomes available. Over time, the interface learns from common claims issues and responses and adjusts to improve the alerts and recommendations.
Compared to the traditionally reactive and time-consuming claims management process, this tool uses predictive analytics to provide a data-driven experience for adjusters, which prepares them for potential issues to reduce the total cost of risk.
Measuring the Impact of Predictive Analytics
Results show that companies that leverage predictive analytics outperform their peers from a total cost of risk, claim efficiency, and employee satisfaction perspective.
One area where CorVel already sees improved outcomes is in claims litigation, an area that others have not previously tracked in the market. Using predictive analytics to understand litigation risk factors can change the claim’s trajectory and sometimes even help avoid litigation altogether. CorVel’s customers are seeing a nearly 40% reduction in litigation rates due to this early insight.
Assessing pharmacy risk is another area where the Claims Risk Score interface is a game-changing tool. As the opioid epidemic continues, identifying injured workers at risk of addiction is critical. Using AI and machine learning, CorVel analyzes data to identify and flag potentially problematic claims and inappropriate prescribing patterns. Predictive analytics can assess the morphine equivalency of an individual’s drugs, then notify case managers and adjusters when this level approaches potentially dangerous or dependent levels. This risk-scoring technology encourages patient safety and helps customers save on pharmacy costs.
Using advanced technology that integrates across all data streams, CorVel is putting predictive analytics to work for workers’ compensation. The Claim Risk Score interface is unlike any other available predictive analytics platform. The capabilities within CareMC Edge offer cutting-edge tools and actionable insights to all customers. Click here to request a demo of the new CareMC Edge Claim Risk Score interface.
