AI Poised to Revolutionize Hospital Quality Reporting, UC San Diego Study Finds

Research reveals AI's potential to transform hospital reporting processes

University of California - San DiegoOct 21 2024 A pilot study led by researchers at University of California San Diego School of Medicine found that advanced artificial intelligence (AI) could potentially lead to easier, faster and more efficient hospital quality reporting while retaining high accuracy, which could lead to enhanced health care delivery. The study results, published in the October 21, 2024 online edition of the New England Journal of Medicine (NEJM) AI, found an AI system using large language models (LLMs) can accurately process hospital quality measures, achieving 90% agreement with manual reporting, which could lead to more efficient and reliable approaches to health care reporting. Researchers of the study, in partnership with the Joan and Irwin Jacobs Center for Health Innovation at UC San Diego Health (JCHI), 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. 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 personalized care and improve patient access to quality data. As we advance this research, we envision a future where quality reporting is not just efficient but also improves the overall patient experience." Aaron Boussina, postdoctoral scholar 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 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. "We remain diligent on our path to leverage technologies to help reduce the administrative burden of health care and, in turn, enable our quality improvement specialists to spend more time supporting the exceptional care our medical teams provide," said Chad VanDenBerg, study co-author and chief quality and patient safety officer at UC San Diego Health. 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. 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 with UC San Diego. This study was funded, 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. University of California - San Diego Journal reference: Boussina, A., et al. (2024) Large Language Models for More Efficient Reporting of Hospital Quality Measures. NEJM AI. doi.org/10.1056/AIcs2400420.

AI could transform how hospitals produce quality reports

A pilot study led by researchers at University of California San Diego School of Medicine found that advanced artificial intelligence (AI) could potentially lead to easier, faster and more efficient hospital quality reporting while retaining high accuracy, which could lead to enhanced health care delivery. The study results, published in the October 21, 2024 online edition of the New England Journal of Medicine (NEJM) AI, found an AI system using large language models (LLMs) can accurately process hospital quality measures, achieving 90% agreement with manual reporting, which could lead to more efficient and reliable approaches to health care reporting. Researchers of the study, in partnership with the Joan and Irwin Jacobs Center for Health Innovation at UC San Diego Health (JCHI), 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. "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 personalized care and improve patient access to quality data," said Aaron Boussina, postdoctoral scholar and lead author of the study at UC San Diego School of Medicine. "As we advance this research, we envision a future where quality reporting is not just efficient but also improves the overall patient experience." 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. "We remain diligent on our path to leverage technologies to help reduce the administrative burden of health care and, in turn, enable our quality improvement specialists to spend more time supporting the exceptional care our medical teams provide," said Chad VanDenBerg, study co-author and chief quality and patient safety officer at UC San Diego Health. 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. 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 with UC San Diego. This study was funded, 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.

Pilot study finds AI could transform how hospitals produce quality reports

A pilot study led by researchers at University of California San Diego School of Medicine found that advanced artificial intelligence (AI) could potentially lead to easier, faster and more efficient hospital quality reporting while retaining high accuracy, which could lead to enhanced health care delivery. The study results, published in the October 21, 2024 online edition of the NEJM AI, found an AI system using large language models (LLMs) can accurately process hospital quality measures, achieving 90% agreement with manual reporting, which could lead to more efficient and reliable approaches to health care reporting. Researchers of the study, in partnership with the Joan and Irwin Jacobs Center for Health Innovation at UC San Diego Health (JCHI), 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. "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 personalized care and improve patient access to quality data," said Aaron Boussina, postdoctoral scholar and lead author of the study at UC San Diego School of Medicine. "As we advance this research, we envision a future where quality reporting is not just efficient but also improves the overall patient experience." 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. "We remain diligent on our path to leverage technologies to help reduce the administrative burden of health care and, in turn, enable our quality improvement specialists to spend more time supporting the exceptional care our medical teams provide," said Chad VanDenBerg, study co-author and chief quality and patient safety officer at UC San Diego Health. 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. 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 with UC San Diego.