JAMIA open · 2026
Development and validation of an algorithm to identify severe sepsis onset from electronic medical records.
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Homayouni Ramin, Morrell Shane, Karsten Joel David, Chhabra Riya, Bozyk Paul D
Аннотация
OBJECTIVE: To develop and evaluate an automated algorithm to identify sepsis onset, referred to as time-zero (t0), from the electronic medical records (EMR).
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 t0 (mean: -0.60 hour, 95% CI, -0.74 to -0.45).
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.
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 t0 (mean: -0.60 hour, 95% CI, -0.74 to -0.45).
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.