Combat rising workers’ compensation insurance costs with data-driven benchmarks.
Workers’ comp (WC) payers have been chasing medical cost reduction in workers’ compensation for decades.
Claims systems typically do not capture accurate data on attorney representation.
患者にとって、脚の骨折の原因がスキーであろうと仕事であろうと、その痛みは同じであり、同様の治療が必要です。しかし、職場で発生した傷害は、雇用主がその費用を負担します。
労働者災害補償保険（Workers’ compensation、WC）の支払者である雇用主と保険会社などは、同じ症状について団体健康保険の支払者よりも60%から100%高額な医療費用を支払うことがよくあります。なぜでしょうか？労災補償業界は、団体健康保険における類似症状の費用および治療管理に対して不明朗な状況です。
人工知能（AI）およびプレディクティブ分析の進化、また、団体の健康データへのアクセスのおかげで、労災保険の支払者は、適切なプロバイダーが適切な治療計画を適正価格で実施することにより、保険金請求費用を抑える機会を迅速に特定することが可能になりました。
新たなテクノロジーを研究している雇用主や保険会社のために、団体健康保険に対する労災保険のベンチマーキングにより価値を創出する方法に関して、よくある質問のいくつかにお答えします。
To the patient, a broken leg will have similar pain and need similar treatment whether it happened on the ski slopes or on the job. But if the injury happened at work, the employer will be on the hook for the costs.
Workers’ compensation (WC) payers, including employers and insurers, often pay 60% to 100% higher medical costs than group health payers for the same condition. Why? The workers’ compensation industry lacks transparency into the cost and care management of similar episodes in group health.
Thanks to advances in artificial intelligence (AI) and predictive analytics, as well as accessibility to group health data, workers’ compensation payers can quickly identify opportunities to lower claim costs through the right treatment plan, the right provider to execute the treatment plan, and the right price.
For employers and insurers exploring the new technology, let’s answer some frequently asked questions about how to create value by benchmarking WC against group health.
How can workers’ compensation payers use group health data for medical benchmarking?
Workers’ compensation payers can use medical benchmarking to assess their performance relative to group health payers. With the group health information, WC payers can compare utilization (quantity and mix of services) and unit cost (price per service) to identify areas of overspend. With this understanding, WC payers can develop strategies to reduce medical costs.
How can AI and predictive analytics help workers’ compensation payers tap into group health data?
Leveraging the vast group health data set requires the aid of technology. Increasingly, artificial intelligence (AI) and predictive analytics are employed in models that score or triage claims based on the “hidden” characteristics of a claim, which have the potential for developing high costs. Analytic platforms with the most reliable predictive capacity use text mining and machine learning to uncover information in unstructured text data as well as structured or coded data. Claims with high scores can then be directed to more experienced adjusters.
How have workers’ compensation payers typically analyzed medical costs?
In the past, workers’ compensation payers typically reviewed their performance in relation to their own historical data or to industry WC peers. While a reasonable comparison, it has significant limitations.
If a WC payer, and the industry at large, have always overpaid for medical services, an analysis of WC data may not help identify new opportunities to reduce the cost of medical services.
Additionally, all but the largest employers and insurers lack enough sample size in their WC data to credibly analyze all conditions in WC. Further, the data is not large enough to drill down into regional differences in cost.
A large, nationwide data set of group health claims enables WC payers to perform more meaningful benchmarking, but that information wasn’t available to WC payers until now.
In a low-volume area, such as Montana, a WC payer may only have a few historical data points for a given condition, such as a torn rotator cuff. By tapping into group health data, we can use much larger samples of similar patients with rotator cuff tears in group health.
What are the benefits of using group health data for medical benchmarking workers’ comp claims?
Group health-based benchmarks provide actionable insights that allow adjusters to find and utilize the:
- Right treatment plan: Better understand utilization for a given condition.
- Right provider: Identify the highest-performing providers for a given specialty near an injured worker.
- Right price: Negotiate the cost of specific procedures.
Group health based medical benchmarks provide an additional point of leverage for controlling workers’ compensation claim costs, especially on high-scoring claims and/or those with less effective providers. Alerted to these cases, adjusters can deploy value-based care strategies and use optimal care pathways and managed care techniques to set goals for utilization. Adjusters can also understand potential episode costs in order to direct care and negotiate fees where appropriate.
What are some other considerations when comparing group health and workers’ comp claim data?
When drawing on group health data, WC payers should be prepared for differences in:
- Patient cost sharing: Group health plans often have copays and deductibles, while WC benefits are first-dollar. Benchmarks should include patient pay plus insurer pay.
- Benefits for lost wages: WC benefits also include coverage for lost wages for the duration of the disability. When benchmarking against group health, workers’ comp payers will only be able to benchmark medical costs.
- Attorney involvement: WC claims can involve attorneys representing the injured workers. Claims with attorney involvement are often associated with higher-cost outcomes.
- Cost management: Given the size of the group health industry, significant investments and innovation have occurred to help control costs, more so than in the smaller WC market.
Due to these differences, WC payers are unlikely to fully close the 60% to 100% gap between group health and workers’ comp costs. Still, by combining data and AI with a sound strategy, there is room for considerable cost savings.
How can we use medical benchmarking to achieve savings in workers’ comp?
To help close the cost gap with group health, WC payers first need access to the group health data. Second, they need AI and predictive analytics to identify opportunities for cost savings. And finally, they need subject matter experts in WC and group health to help them develop strategies to achieve savings.
On a strategic level, group health-based medical benchmarks increase visibility into provider performance compared with conventional benchmarks, and give workers’ comp payers greater ability to:
- Build more effective provider networks
- Tailor tiers within networks with greater precision
- Negotiate provider contracts based on data-driven performance comparisons
- Align networks with the most effective providers in states where care can be directed
- Develop value-based arrangements based on quality measures
WC payers can also track the gap between group health and their workers’ comp medical spend over time, monitor provider performance, and see whether utilization or unit cost is driving differences in cost.
By embracing data and AI, workers’ comp payers can push the WC insurance industry forward. With more data, employers and insurers can resolve workers’ compensation claims more quickly and at a lower cost, which is better for both employers and their employees.
About Nodal
Nodal® Medical Benchmarking for Workers' Comp helps clients proactively manage their WC medical spend by leveraging Milliman’s proprietary Group Health database. Nodal Medical Benchmarking for Workers' Comp compares the cost of WC medical services to Group Health, allowing clients to identify opportunities to better manage medical costs.