Remarkable technologies entering the market could radically transform claims management in workers’ compensation. These revolutionary innovations include natural language processing, virtual reality, cost-effective sensors, intricate reporting mechanisms, and especially artificial intelligence or AI.

Last week’s Leaders Speak article stressed the need to remain focused on patient outcomes while navigating the age of AI. Organizations must be careful not to fall prey to the “shiny object syndrome” by simply plugging in technology to solve a problem that isn’t even there. They need to carefully examine the technology’s ability to positively affect the company’s workflow and the patient’s outcomes.

Answering the important questions posed in last week’s article will help you determine if a particular technology indeed aligns with your mission. This week, we delve deep into the next three steps that can improve the implementation, and more importantly, adoption and user satisfaction with the AI tool.

Staying mindful of biases and their potential impact

When harnessing the power of AI, it is imperative to acknowledge and address the inherent issue of bias that can permeate its outcomes. The foundation for AI predictions rests upon the data it processes, underscoring the importance of curating a robust and representative dataset. Thus, the quality of the dataset used for training the AI engine directly influences the accuracy and fairness of the AI’s conclusions.

Just as our biases subtly influence the instructions given to children, biases that may be present in our training data also have the potential to infiltrate future results, leading to biased AI predictions. It then becomes paramount to remain cognizant of these biases and their potential impact on AI systems. And while complete eradication of bias is impossible, fostering awareness enables us to take proactive measures.

Furthermore, efforts should be dedicated to not only identifying biases but also mitigating their effects. Ensuring transparency and accountability in the AI development process is essential. It is incumbent upon developers to comprehensively assess their datasets, identify potential sources of bias, and implement corrective measures. By meticulously scrutinizing the training process and using curated synthetic data sets, companies can minimize unjust prejudices and bolster AI’s functionality.

Designing with the end user in mind and facilitating meaningful interaction

As with any new processes and technology, AI adoption often faces resistance, particularly among experienced professionals who believe their judgment supersedes automated systems. This is especially evident in the realm of workers’ compensation, where adjusters’ expertise is highly valued. Crafting AI solutions with the end users in mind then requires a thoughtful approach to overcome the initial skepticism and ensure meaningful adoption.

A strategy to encourage adoption is to design AI interactions that can dialogue with claims managers similar to the way adjusters consult with each other on complex cases. A well-designed AI can engage adjusters in a Q&A dynamic where context is supplemented and personalization is prioritized. Here, AI’s role is a collaborative one, allowing adjusters to query AI about predictive drivers for the injury, or request examples of similar cases to broaden their insights.

When claims managers can pose questions, refine queries, and draw on AI’s rich bank of information, AI becomes a conversational partner and a rich research resource instead of a mere prediction engine. This collaborative approach breaks down barriers to acceptance. Instead of replacing human expertise, AI complements it by taking that gut feeling adjusters develop over time, systemizing it, and scaling it.

Contextualizing outcomes as a supplementary tool

AI undoubtedly has the potential to serve as a valuable addition to our toolset, complementing existing interventions and enhancing the diligence and effectiveness of our operations. Its power lies in aggregating accumulated knowledge and insights to drive informed choices and decision-making.

All things considered, however, employing AI also demands a nuanced perspective. It’s crucial to avoid absolute judgments – AI isn’t inherently right or wrong. It is also neither the end-all and be-all nor a one-size-fits-all solution, and users must steer clear of blind adherence to AI-generated responses. After all, no two cases are exactly the same and results must be contextualized within the appropriate actions. Moreover, it’s important to break down informational silos within an organization and across stakeholders to improve communication and data-sharing in order to maximize results

Striking harmony between the use of AI and a patient-first ethos

Ultimately, the successful integration of AI within the workers’ compensation industry hinges on three crucial considerations that form the cornerstone of our approach: staying acutely mindful of biases and their potential impact on outcomes, designing solutions to foster meaningful interactions, and using AI-generated results as a supplementary tool in driving outcomes.

At the end of the day, our ethos is patient-first, not AI-first, and our primary purpose is to guide patients back to work safely post-injury. Remaining steadfast in this mission, however, should not preclude us from embracing transformative technologies. And as we navigate this new AI-driven landscape, our perspective should extend beyond mere technology adoption and more towards strategically employing AI to influence better outcomes across the patient journey.

Three Key Considerations for the Successful Integration of AI in Workers’ Comp. WorkCompWire.com, https://www.workcompwire.com/2023/09/sandip-chatterjee-three-key-considerations-for-the-successful-integration-of-ai-in-the-workers-compensation-industry/.