Managing and mitigating medical claim costs: 5 key applications of big data

Healthcare costs account for more than three trillion dollars in spending, and medical costs continue to outpace inflation. When we dig into workers’ compensation claims data, we see that medical also outpaces indemnity costs. According to the National Council on Compensation Insurance, in 1981, 57% of workers’ compensation claim costs were driven by indemnity, with 43% of costs coming from medical. By 2015, the dynamic had shifted, with medical accounting for 58% of claim costs.

From a general healthcare perspective, the accessibility of rich data in electronic medical records (EMR) can help medical providers make more informed treatment decisions and improve the quality of care delivered to patients. Healthcare systems have started to recognize the value of patient data, as well as the risks associated with it, within their organizations. 

While medical providers continue to move toward having key patient information available at their fingertips, employers and workers’ compensation insurers have a more limited view. General healthcare providers may focus on underlying conditions, while occupational medicine doctors will focus on work-related injuries. As a result, disjointed care may be provided to injured workers with unintended consequences (e.g., drug interactions and overdoses). 

Even with this constraint, insurers can help injured workers and employers by leveraging the power of the robust data available. The ability to capture and aggregate key claim information, along with other integrated medical management solutions, can help promote quality care while also managing and mitigating medical costs.

Five key benefits of big data to help manage medical costs:

1. Appropriate and accurate treatment and billing – Systematically bringing multiple data sources together (e.g., claims data, medical documents and bills and standards of care) can help verify compensability and medical necessity of all charges. Streamlining the ability to cross-reference this type of information can also help claims professionals more quickly identify when an injured worker’s recovery has plateaued so that they can proactively assist in the medical management of the claim.
2. Timely and targeted intervention – Claim outcomes can be affected by a series of factors at different points in time. Predictive models, drawing on a variety of structured (e.g., date) and unstructured (e.g., claim notes) data points can help identify potential high-severity claims and notify claim professionals that a certain action or focused expertise (i.e., nurse case management) may be beneficial to the claim.
3. Fraud, waste and abuse identification – The ability to analyze data across claims helps enable the identification of fraud, waste and abuse (e.g., providers who consistently use the costliest and highest levels of treatment). Thwarting these behaviors can positively impact customers’ loss costs and create opportunities for recovery.
4. High-quality medical providers – Aggregated provider and claim data creates the opportunity to develop an outcome-based medical provider network designed to deliver quality care to injured workers and promotes early return to work.
5. Distinctive risk insights – Benchmarking key drivers of medical claim costs can help customers identify challenges and opportunities to leverage claims best practices, appropriate medical management services and preventative Risk Engineering solutions.