WorkCompWire Leaders Speak: Navigating Our AI Future in Workers’ Compensation (Part One)

Posted on by myMatrixx
Navigating AI in Workers' Compensation

For anyone watching, 2023 has undoubtedly been the year of Artificial Intelligence (AI). Although it has been used for years in fields ranging from finance to manufacturing to personal assistants such as Siri and Alexa, the public launch of ChatGPT by OpenAI last November marked a real turning point in the way society and business view this technology. ChatGPT, or generative pre-trained transformer, is a large language model (LLM) developed to provide detailed and articulate responses to nearly any question, and its incredibly human-like and knowledgeable answers have left many asking if the era of intelligent machines has arrived.

With a sizable portion of AI-activity likely to be directed into the health care and insurance fields, it is crucial for leaders, stakeholders, clinicians and claims professionals in workers’ compensation to understand this technology and the potential impact it will have on our sector. Specifically, in the area of care and management of injured patients, there are tremendous opportunities for AI-assisted technologies to improve patient and client experience, reduce fraud, waste, and abuse, streamline workflows, analyze medical data and create better outcomes through predictive modeling.

Amid industry concerns such as an aging workforce and labor shortage, AI brings unique opportunities for improving efficiency and quality of care for workers’ compensation organizations that can embrace innovation. In the first part of a two-part look at AI in workers’ compensation, Cliff Belliveau, myMatrixx Chief Innovation Officer, examines some of the most exciting ways these advancements can shape the future of workers’ compensation, including:

  1. Virtual Assistance Support for Claims Management

  2. Reduction of Fraud Waste and Abuse at Large Scales

  3. Automated and Streamlined Workflows for Claims Professionals

  4. Processing and Analyzing Medical Data

  5. Predicting Outcomes and Improving Treatment Models