Medical Record Abstraction for Quality Improvement in Sepsis Care Using Artificial Intelligence: A Cluster - Report - MDSpire

Medical Record Abstraction for Quality Improvement in Sepsis Care Using Artificial Intelligence: A Cluster

  • By

  • Aaron Boussina

  • Claire Allison

  • Kimberly Quintero

  • Sonia Jain

  • Chad VanDenBerg

  • Michael Hogarth

  • Amy M. Sitapati

  • Karandeep Singh

  • Atul Malhotra

  • Michael T. McCurdy

  • Christopher A. Longhurst

  • James S. Ford

  • Theodore Chan

  • Paul Ishimine

  • Richard Childers

  • Shamim Nemati

  • Gabriel Wardi

  • June 25, 2026

  • 0 min

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Clinical Report: Utilizing AI for Quality Enhancement in Sepsis Management

Overview

This study investigates the use of artificial intelligence (AI) to enhance the quality of sepsis management through improved measurement of the Severe Sepsis and Septic Shock Management Bundle (SEP-1).

Background

Sepsis remains a leading cause of mortality and morbidity globally. The current methods for measuring quality of care, particularly the SEP-1 measure, are labor-intensive and often lack timely feedback.

Data Highlights

No numerical data or trial results are provided in the source material.

Key Findings

  • AI can accurately abstract complex quality measures from unstructured clinical documentation.
  • The SEP-1 measure is complex and costly to implement, requiring significant person-hours for quality reporting.
  • Timely feedback on sepsis care quality using AI may improve performance metrics.
  • Standard reporting mandates by CMS require only 20 cases per month.
  • The study was conducted in two academic medical centers within the UCSD health system.

Clinical Implications

The integration of AI in sepsis management may reduce the burden of manual data abstraction and improve the timeliness of quality feedback.

Conclusion

The study suggests that AI-enabled approaches to measuring sepsis care quality could enhance performance metrics.

Related Resources & Content

  1. Frontiers in Digital Health, 2026 -- Artificial intelligence based predictive models for early sepsis detection in intensive care units: a scoping review
  2. Critical Care (Springer), 2025 -- Identification of Clinical Subphenotypes in Sepsis Through Mixed Data Analysis and Treatment Response Variability: A Cluster Analysis from Multicenter Observational Research
  3. Journal of General Internal Medicine, 2026 -- Cracks in the AI Crystal Ball: Why Clinical Prediction Tools Fall Short in the Real World
  4. conexiant -- FDA Clears AI Sepsis Warning System
  5. Surviving Sepsis Campaign Adult Guidelines | SCCM
  6. Electronic Sepsis Screening Among Patients Admitted to Hospital Wards: A Stepped-Wedge Cluster Randomized Trial - PubMed
  7. An artificial intelligence-powered learning health system to improve sepsis detection and quality of care: a before-and-after study | npj Digital Medicine
  8. Surviving Sepsis Campaign Adult Guidelines | SCCM
  9. Electronic Sepsis Screening Among Patients Admitted to Hospital Wards: A Stepped-Wedge Cluster Randomized Trial - PubMed
  10. An artificial intelligence-powered learning health system to improve sepsis detection and quality of care: a before-and-after study | npj Digital Medicine

Original Source(s)

Medical Record Abstraction for Quality Improvement in Sepsis Care Using Artificial Intelligence: A Cluster

  • by Aaron Boussina, Claire Allison, Kimberly Quintero, Sonia Jain, Chad VanDenBerg, Michael Hogarth, Amy M. Sitapati, Karandeep Singh, Atul Malhotra, Michael T. McCurdy, Christopher A. Longhurst, James S. Ford, Theodore Chan, Paul Ishimine, Richard Childers, Shamim Nemati, Gabriel Wardi

  • June 25, 2026

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