A pilot study led by researchers at the University of California San Diego School of Medicine found that advanced artificial intelligence (AI) could potentially lead to easier, faster, and easier reporting of hospital quality. more efficient while maintaining high accuracy, which could lead to better healthcare delivery.
The results of the study, published in the October 21, 2024 online edition of New England Journal of Medicine (NEJM) IAfound that an AI system using large language models (LLM) can accurately process hospital quality metrics, achieving 90% agreement with manual reporting, which could lead to more efficient and effective approaches. reliable health care reporting.
Study researchers, in partnership with the Joan and Irwin Jacobs Center for Health Innovation at UC San Diego Health (JCHI), found that LLMs can perform precise abstractions for complex quality measures, especially in the difficult environment of the Centers for Medicare and Medicaid. Services (CMS) SEP-1 measure for severe sepsis and septic shock.
Integrating LLMs into hospital workflows promises to transform healthcare delivery by making the process more real-time, which can improve personalized care and patient access to quality data. As we move forward with this research, we envision a future where reporting quality is not only effective, but also improves the overall patient experience.
Aaron Boussina, postdoctoral researcher and lead author of the study at UC San Diego School of Medicine
Traditionally, the abstraction process for SEP-1 involves a meticulous 63-step assessment of detailed patient records, requiring weeks of effort by multiple reviewers. This study found that LLMs can significantly reduce the time and resources required for this process by accurately analyzing patient records and generating crucial contextual information in seconds.
By meeting the complex requirements of quality measurement, researchers believe the results pave the way for a more efficient and responsive health system.
“We remain diligent in our journey to leverage technologies to help reduce the administrative burden of healthcare and, in turn, enable our quality improvement specialists to spend more time supporting exceptional care provided by our medical teams,” said co-study Chad VanDenBerg. author and chief quality and patient safety officer at UC San Diego Health.
Other key findings from the study revealed that LLMs can improve efficiency by correcting errors and speeding up processing time; reduce administrative costs by automating tasks; enable near real-time quality assessments; and are scalable in various healthcare settings.
Future steps include the research team’s validation of these findings and their implementation to improve reliable data and reporting methods.
Co-authors of this study include Shamim Nemati, Rishivardhan Krishnamoorthy, Kimberly Quintero, Shreyansh Joshi, Gabriel Wardi, Hayden Pour, Nicholas Hilbert, Atul Malhotra, Michael Hogarth, Amy Sitapati, Karandeep Singh and Christopher Longhurst, all of UC San Diego.
This study was supported, in part, by the National Institute of Allergy and Infectious Diseases (1R42AI177108-1), the National Library of Medicine (2T15LM011271-11 and R01LM013998), and the National Institute of General Medical Sciences (R35GM143121 and K23GM146092) and JCHI. .
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Journal reference:
Boussina, A., et al. (2024) Extended Linguistic Models for More Effective Reporting of Hospital Quality Metrics. NEJM AI. doi.org/10.1056/AIcs2400420.