Researchers have found an AI system using large language models (LLMs) can accurately process hospital quality measures, achieving 90 per cent agreement with manual reporting, which could lead to more efficient and reliable approaches to health care reporting.
Researchers found that LLMs can perform accurate abstractions for complex quality measures, particularly in the challenging context of the Centers for Medicare & Medicaid Services (CMS) SEP-1 measure for severe sepsis and septic shock.
Traditionally, the abstraction process for SEP-1 involves a meticulous 63-step evaluation of extensive patient charts, requiring weeks of effort from multiple reviewers.
This study found that LLMs can dramatically reduce the time and resources needed for this process by accurately scanning patient charts and generating crucial contextual insights in seconds.
By addressing the complex demands of quality measurement, the researchers believe the findings pave the way for a more efficient and responsive health care system.
“The integration of LLMs into hospital workflows holds the promise of transforming health care delivery by making the process more real-time, which can enhance personalised care and improve patient access to quality data,” said Aaron Boussina, lead author of the study at the University of California – San Diego.
“As we advance this research, we envision a future where quality reporting is not just efficient but also improves the overall patient experience.”
Other key findings of the study found that LLMs can improve efficiency by correcting errors and speeding up processing time; lowering administrative costs by automating tasks; enabling near-real-time quality assessments; and are scalable across various health care settings.
Future steps include the research team validating these findings and implementing them to enhance reliable data and reporting methods.
