The Benefits and Challenges of the AI Revolution in Workers’ Compensation

Artificial intelligence promises to redefine how workers’ compensation claims are managed, offering unprecedented efficiencies and data modeling support. It will ultimately streamline claims operations, enhance decision-making and improve outcomes across the ecosystem. However, this transformation also comes with challenges.

When they’re utilized properly, generative AI systems’ ability to sift through massive amounts of information and offer predictive insights in real time can enhance decision-making processes, make recommendations on next treatment and administrative steps based on data-driven scenarios, determine when a case has reached clinical maximal medical improvement (MMI), return an advisory rating for beginning the settlement process, and test the skill and knowledge set of the physician.

AI can generate multiple options for each case, allowing users to project, evaluate and play out different outcomes and make more-informed decisions. This contributes to a more streamlined system, offering faster claim resolution and improved medical and financial outcomes for injured workers.

AI’s ability to analyze large datasets also helps in pattern recognition, user behavior and game theory applications, leading to better predictions of the future trajectory of a claim. It may also leverage zip codes for medical provider network availability and even predict the likelihood of access to full or partial treatments. Said another way, AI will be able to find and locate “treatment deserts” for injured workers and project the lack of treatment as a reflection in the claim settlement for permanent disability.

For instance, based on historical data, AI can assess the likelihood of a worker requiring surgery, responding positively to conservative treatments or being able to access a surgeon at all. It can factor in comorbidities such as depression, obesity and chronic metabolic syndrome, and provide predictions about their influence on recovery and overall claim costs. It can predict the likelihood that an injured worker will drive beyond 100 miles for a single consult (or a second or third office visit) before having their infrastructure for travel fail.

A Boost for Stakeholders

AI can streamline the efforts of every participant in the workers’ compensation system.

Physicians: AI can introduce standardization in the form of uniform physical exams, consistent diagnoses and determinations of causation based on the best available medical evidence.

AI engines could also prompt a doctor for more information when conducting a physical exam or diagnostic testing and ultimately return a percentage of completeness to the stakeholders.

These AI “brains” could also be used to customize claim ratings that reflect various legal standards and past court cases, such as the 3% California pain add-on or the Nunes case, which requires new analysis for functional loss and earning capacity.

Furthermore, AI can assist in the complicated and often contested determinations of apportionment and impairment values.

Adjusters: By providing a more structured and standardized approach, AI enables adjusters to assess the near-term and future impact of a claimant’s choices. For example, if an injured worker opts for surgery instead of conservative care, AI could play out multiple scenarios, considering comorbidities and providing insights into how these decisions could influence the cost and duration of the claim.

Legal professionals: Attorneys might use AI to assist with client interviews, enabling them to ask more pointed and relevant questions during depositions. Judges, too, could benefit from AI’s ability to review case law quickly and accurately.

Challenges for AI in Workers’ Compensation

Despite the potential benefits, several challenges could hinder the widespread adoption of AI in workers’ compensation.

• Data quality and accuracy: Inaccurate or incomplete databases can lead to erroneous recommendations and outcomes. Ensuring that AI operates on the most reliable and accurate datasets is crucial to its success.
• Parameter control: AI systems generate vast amounts of data, but there is a need for clear guidelines on how these systems are trained and what data they are allowed to access. Without them, AI systems may provide irrelevant or incorrect information, leading to flawed decision-making. AI must be configured to return only the most relevant and accurate information.
• Transparency and documentation: For AI to be trusted, stakeholders must be able to validate and verify sources of data. Injured workers, employers, physicians, adjusters and attorneys will need assurances that AI-generated recommendations are based on legitimate, well-sourced information.
• Intellectual property/privacy/copyright infringement: AI most certainly will raise challenges around the ownership of a doctor’s findings, opinions and determinations. Although patent law is outside the traditional scope of workers’ compensation, it will need to be addressed as AI becomes more prevalent in the field.
• Standardization: As AI continues to make its way into workers’ compensation, it will be necessary to develop a standardized framework for its use, both clinically and legally. The workers’ compensation system will need clear guidelines on when and how AI can be applied, as well as protocols for documenting and preserving intellectual property rights.

AI Is the Future of Workers’ Compensation

The future of workers’ compensation will likely depend on AI systems that operate on accurate data and are transparent in their recommendations. By addressing the challenges of data quality, intellectual property preservation and regulatory standardization, we can ensure the ethical and effective integration of AI into the workers’ compensation system. As AI evolves, so too will the opportunities and challenges that arise from its implementation, requiring ongoing collaboration between stakeholders in medicine, law and technology.

The future looks promising, but the AI road ahead will require careful navigation to balance innovation with the preservation of rights and ethical standards. &