Medicare Set-Aside and AI: A Powerful Combination

May 24, 2024

By Kim Wiswell

Efficiently managing Medicare Set-Aside Arrangements (MSAs) is a formidable challenge within workers’ compensation programs. Adjusters struggle with time-consuming manual documentation and complex compliance requirements and the need for accurate forecasting of treatments and cost estimation further compounds the complexity. In this article, we will look at the powerful impact of integrating AI and automation into the MSA process, and how it can effectively address these hurdles and make the MSA process more efficient.

Challenges: Manual Documentation & Complex Regulations

Manual Documentation

The conventional MSA application process relies heavily on manual documentation. This involves the painstaking tracking of medical records, treatment history, and payment reports. This tedious approach is not only time-consuming, but also prone to inaccuracies, often requiring significant human resources. In addition, manual documentation increases the risk of missing critical information, leading to delays and potential compliance issues.

Claims assistants or adjusters often spend a significant amount of time on MSA requests, ranging from an average of two to ten hours per referral, depending on factors like claim complexity, age, and the volume of medical records involved. Extended follow-up correspondence for clarification of body parts accepted under the claim or missing records can further extend this time.

Complex Regulatory Landscape

Another significant challenge is the navigation of the complex regulatory landscape of Medicare compliance. To be successful, the MSA application process requires meticulous attention to detail and expertise in interpreting Medicare’s guidelines and regulations. Compliance errors can lead to penalties, delays in MSA approval by CMS, and heightened administrative burdens for all involved stakeholders.

Solution: Harness AI

Automation of Documentation Process

By automating the documentation process, AI significantly reduces the manual effort needed to produce MSA determinations. Advanced algorithms can quickly analyze medical records, treatment histories, and billing information to generate comprehensive MSA documentation accurately. By automating repetitive tasks, AI reduces the likelihood of errors, accelerates processing times, and frees up resources for more strategic activities.

Compliance Management

AI technologies offer sophisticated compliance management capabilities, which allows all parties to navigate Medicare regulations with ease. AI-powered platforms continuously monitor changes in Medicare policies, ensuring MSA documentation remains up-to-date and compliant. Additionally, AI algorithms can identify potential compliance issues proactively, allowing stakeholders to address them promptly and mitigate risks effectively.

Integrating AI technologies into MSA operations can significantly impact MSA workflows. By automating the information and documentation collection and simplifying compliance management, AI streamlines both the MSA referral process, as well as internal operations, improving efficiency, and ensuring adherence to Medicare regulations. As the complexity of modern healthcare continues to evolve, leveraging AI becomes increasingly important for optimizing MSA processes and achieving better outcomes for all involved.

Challenges: MSA Treatment Forecasting and Cost Estimations

Treatment Forecasting Inefficiencies
The current MSA process is laborious. First, a specialized allocator physically goes through the injured worker’s medical records, creating a summary of the injury and the medical treatment. For each treatment, the allocator must determine whether the recommended treatment has been provided to the injured worker. Any unfulfilled recommendations must be included in the MSA. Next, a treatment plan is developed based on physician recommendations for specific treatments such as surgeries, diagnostic testing, hospitalizations, etc., as well as treatment corresponding to CMS’s standardized guidelines. Last, the allocator prices the services and drugs under the treatment plan using state fee schedules and other pricing resources specified by CMS and recommends an MSA amount for the claim.

All of this can take several days to more than a week, which unfortunately leads to delays in claim closure and settlement funds being paid to the injured worker. In addition, the existing approach often leads to unpleasant surprises for claims managers. Unrecognized treatment recommendations often, consequently, increase the overall MSA amount, sometimes making the settlement impossible.

Cost Projection Inaccuracies
It is challenging to accurately estimate future medical costs within the current MSA framework. Most services are priced out based on state workers’ compensation fee schedules or, when unavailable, usual, customary and reasonable (UCR) fees for the jurisdiction. As fee schedules are unique to each state, and fees are updated as often as quarterly, determining the correct pricing can be a complex process. Also, as the claimant’s medical condition and health status change over time, often necessitating new and different treatments, the amount allocated in an MSA may not accurately reflect the future funds needed. Compounding this, CMS requires MSAs associated with settlements to have been done within the past six months. After this point, they are considered stale-dated, leading to additional costs for the claims payer, both for multiple MSA updates and increasing treatment fees over the course of lengthy settlement negotiations and proceedings.

Solution: Harness AI

Improved Identification of Future Medical Treatments

AI solutions can significantly improve MSA operations by addressing inefficiencies in the identification of future medical treatments. By analyzing vast amounts of medical reports, generative AI can detect treatment recommendations within the records, compare them to structured medical payment data for treatment already provided to the injured worker, and flag treatment yet to be provided. These insights allow allocators to readily identify services and drugs for inclusion in the MSA.

For example, by analyzing historical treatment data, AI can determine specific treatment frequencies for evaluations, therapy, and surgical revisions, as well as the injured worker’s current drug regimen. This data helps MSA allocators make better decisions when forecasting treatment and drugs, which leads to improved cost savings and more accurate funding of the MSA.

Enhanced MSA Cost Projections

MSA cost projections can also be more accurate by leveraging AI. Predictive analytics can monitor trends, analyzing information in real time. In cases where settlement hasn’t occurred, it can automatically recommend changes to the MSA to meet the changing medical conditions and related treatment needs. AI also notes important things that can change, such as evolving treatment plans, how medication is used, and how much things cost. It can also identify and flag escalating cost-drivers such as costly surgeries or procedures early on with suggested actions on how to address each cost driver to mitigate costs.

As healthcare evolves, the integration of AI in the MSA process is key to streamlining operations and ensuring the success of workers’ compensation programs. AI offers a promising solution to the challenges of manual documentation, compliance management, treatment forecasting, and cost estimation, ultimately leading to better outcomes for all involved.

About Kimberly Wisell, CMSP-F

With extensive expertise in workers’ compensation, Kimberly Wiswell is proficient in medical bill review, provider networks, data analytics, system design, workflow re-engineering, and operations management. She has held executive roles at Coventry, CLARA Analytics, MEDVAL, Fair Isaac, Health Net, Fremont Compensation Insurance Group, CompPartners, Beech Street, and Medata. Her consultancy clients include the California Workers’ Compensation Institute, the California Division of Workers’ Compensation, Kaiser Permanente, Sutter Health, and Optum. Kimberly served as president of the National Medicare Secondary Payer Network and is a respected speaker on MSP matters, also providing consulting services to the Center for Medicare and Medicaid Services (CMS). Follow Kimberly on LinkedIn

About Gradient AI
Gradient AI is a leading provider of proven artificial intelligence (AI) solutions for the insurance industry. Its solutions improve loss ratios and profitability by predicting underwriting and claim risks with greater accuracy, as well as reducing quote turnaround times and claim expenses through intelligent automation. Unlike other solutions that use a limited claims and underwriting dataset, Gradient AI’s software-as-a-service (SaaS) platform leverages a vast industry data lake comprising tens of millions of policies and claims. It also incorporates numerous other features including economic, health, geographic, and demographic information. Customers include some of the most recognized insurance carriers, MGAs, MGUs, TPAs, risk pools, PEOs, and large self-insured employers across all major lines of insurance. By using Gradient AI’s solutions, insurers of all types achieve a better return on risk. Follow Gradient AI on LinkedIn or X.