Development and validation of an algorithm to identify severe sepsis onset from electronic medical records.

Authors

• Ramin Homayouni, Corewell Health East
• Shane Morrell
• Joel David Karsten, Oakland University William Beaumont School of Medicine Medical Student
• Riya Chhabra, Oakland University William Beaumont School of Medicine Medical Student
• Paul D. Bozyk, Corewell Health East

Document Type

Article

Publication Date

7-18-2026

Publication Title

JAMIA Open

Abstract

OBJECTIVE: To develop and evaluate an automated algorithm to identify sepsis onset, referred to as time-zero (t

MATERIALS AND METHODS: We developed an algorithm to construct a comprehensive timeline of systemic inflammatory response syndrome (SIRS) criteria and organ dysfunction (OD) using structured data, and documentation of infection (DOI) using both structured data and unstructured clinical notes. Algorithm performance was assessed using 2030 manually abstracted adult sepsis cases from a multicenter health system in southeast Michigan.

RESULTS: On average, the algorithm DOI time was significantly earlier than abstractors (mean: -0.33 hour, 95% Cl, -0.55 to -0.11), resulting in a significantly earlier t

DISCUSSION: Automated approaches to analyzing EMR data offer a scalable framework for SEP-1 monitoring, research, and quality improvement.

CONCLUSION: Incorporating unstructured clinical notes improves DOI detection.

Volume

9

Issue

4

First Page

129

Recommended Citation

Homayouni R, Morrell S, Karsten JD, Chhabra R, Bozyk PD. Development and validation of an algorithm to identify severe sepsis onset from electronic medical records. JAMIA Open. 2026 Jul 18;9(4):ooag129. doi: 10.1093/jamiaopen/ooag129. PMID: 42471911.

DOI

10.1093/jamiaopen/ooag129

ISSN

2574-2531

PubMed ID

42471911