Today’s increasingly sophisticated workers’ compensation predictive modeling can accurately forecast which injured workers are most likely to struggle with returning to work, which claims could turn into something severe, and which claims need further investigation.
The insight can make a big difference to both injured workers and the organization. Knowing what is ahead can help prioritize resources, get extra support to those who need it, and avoid costly blunders. When claims close faster, the result is usually a better outcome for everyone.
Faster Resolution, Better Outcomes
The workers’ compensation predictive models are built on a variety of tried-and-true statistical methods that analyze historical data to identify patterns and forecast outcomes. Here are seven predictive models that are helping claims professionals identify potential problems, intervene early, and achieve better results:
- Early severity. Getting the right resources to high-risk injured employees early in the process can change the trajectory of the claim. Early severity models use regression methods to analyze injury type, claimant demographics, and initial medical assessments to predict the severity of a workers’ compensation claim and flag those with the potential to become high risk and/or high cost. That way, you can direct resources – e.g., rehabilitation programs, medical treatments, and additional support – to those claims that will most benefit from early intervention, which will reduce costs and improve outcomes.
- Fraud detection. Workers’ compensation fraud costs the industry approximately $34 billion per year. Fraud detection models leverage machine-learning techniques like decision trees and random forests to sift through workers’ comp claims data for suspicious patterns and anomalies to identify common fraud indicators such as inconsistent information, excessive medical treatments, or repetitive claims from the same individual. Suspicious claims are flagged for further investigation.
- Return to work. The key to a successful return-to-work program is speed – respond quickly to resolve quickly. Return-to-work models analyze the nature of the injury, medical treatments received, and other factors to predict the likelihood of an injured employee returning to work within a specified timeframe. This insight can help jump-start customized rehabilitation plans, vocational training programs, and workplace accommodations to facilitate productive reintegration into the workforce. Quick action also aids in employee recovery, productivity, morale, while reducing costs to the organization.
- Subrogation. Claims leakage – from third-party liability, inefficiencies, or errors – costs the insurance industry millions each year. Subrogation models analyze accident reports, liability information, and other details to identify claims where a third-party is at fault. The models also can determine the likelihood of successfully recouping costs of already-paid claims so you can prioritize your efforts.
- Medical cost projection. Medical costs – including facilities, physician costs, equipment, and supplies – account for the majority of workers’ comp costs. Medical cost projection models analyze historical claim data, medical treatment patterns, and healthcare cost trends to estimate the cost of ongoing treatments, surgeries, medications, rehabilitation services, etc. With an accurate forecast of the total liability of the claim, you can allocate reserves appropriately and incorporate cost-effective strategies for managing medical expenses.
- Litigation risk. Litigated workers’ comp claims are 388% more expensive than non-litigated claims. Injured employees who hire attorneys to handle their workers’ comp cases tend to stay on disability longer, which increases costs and duration of the claim. Litigation risk models use data grouping methods like cluster analysis to analyze complexity, previous litigation history, and jurisdictional considerations to assess the likelihood of a claim escalating into a legal battle. AI-enabled litigation risk models also can help target early settlement negotiations, identify candidates in need of thorough investigations, and flag claims that may need specialized legal support.
- Predictive safety. What if you could identify high-risk areas, anticipate injury trends, and proactively fix problems to prevent injuries from happening in the first place? Predictive safety models estimate the likelihood of future workplace incidents and injuries by applying time-series analysis to historical accident data, near-miss reports, and safety inspection findings. The analysis can help prioritize safety initiatives, allocate resources effectively, and implement targeted interventions.
Tapping into the Power of Unstructured Data
The earliest forms of predictive modeling relied on “structured” data – that is, data organized and formatted into defined categories and metrics that can be understood by simple algorithms like regression models. However, as much as 80% of claims data is unstructured in the form of text, images, media, and other information not easily captured in a traditional row/column format.
With advances in methods that use natural language processing, predictive models are now able to interpret the complexities of unstructured data. Generative AI will allow for even more nuanced decision-making by analyzing the most complex forms of unstructured data.
And those nuances are what can make the claims process more human. Workers’ compensation predictive modeling gives claim professionals a deeper understanding of the person who is injured, what they are going through, and what help is needed. That technology-assisted humanness is what will optimize outcomes for everyone.
For more on optimizing your claims process, download our ebook, Claim Success: Achieving Excellence in Claims Management, and check out Riskonnect’s Claims Management software solution.
