12 Jul Health Payers: Generative AI Will Significantly Impact the Industry

Without a doubt, 2023 is the year of Artificial Intelligence (AI), and the healthcare arena is squarely in the center of it all. While there’s been plenty of press coverage around patient acceptance of AI, there’s much more to be explored around AI and health payers.

Gartner recently issued a study titled, “2Q23 LLMs and Generative AI: U.S. Healthcare Payer Perspectives,” which captures heath payers’ perceptions around large language models (LLMs) and Generative AI. Click here to access the report. (Gartner subscription required).

The study found that 55 percent of healthcare payer CIOs and technology leaders think LLMs will have a transformative or disruptive impact on the healthcare industry overall. However, 75 percent think these technologies will not have the same level of impact on their own organizations.

In addition, natural language analytics, empathy and tone refinement, and language translation are the early LLM implementation use cases that payers are exploring. Future use cases focus on code generation, member self-service and first-draft member communications.

However, while the majority of payers are formally assessing LLM use cases, they also recognize the risks of these technologies with 42 percent prohibiting employees to use AI on enterprise-connected systems.

When implementing new AI-driven use cases, the most critical element that payers should consider is that success is contingent upon the overall quality of all clinical data.

At IMAT Solutions, we believe that without quality data, any new AI solution could potentially provide insights that are incorrect, and be harmful to patients.

As such, we are currently exploring benchmarking studies to assess the validity of using AI generated data to complement our Clinical Data Repository (CDR) compared to the data provided by the Perfect Search technology behind our solutions.

The IMAT Payer Solution already takes the unclean, incomplete, raw clinical data that comes out of the EHR eco-system and dramatically improves the overall data quality.

This is achieved by:
• Increasing the usability of all source data
• Providing data quality improvements with a focus on clinical content
• Leveraging use case inference and algorithm logic for further data usability

Our ultimate goal is to prevent scenarios where an AI system will inadvertently create incorrect information.
When exploring AI solutions or even advancing the current use of health data overall, it’s vital to remember that data quality is of utmost importance.

Got questions about AI for health payers? Our team of experienced health informatics experts is ready to assist. Contact us today.