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Related Concept Videos

Nursing Clinical Information System01:27

Nursing Clinical Information System

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Nursing Clinical Information System (NCIS)
A Nursing Clinical Information System (NCIS) is a specialized type of healthcare information system tailored to meet the unique needs of nursing practice. It incorporates the principles of nursing informatics to streamline information management and improve the quality of care delivery.
Critical attributes of NCIS include:
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Updated: Sep 12, 2025

A Data-Driven Approach to Quantifying Immune States in Sepsis
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Decoding Sepsis: A Technical Blueprint for an Algorithm-Driven System Architecture.

Abdullah Safi1, Mostafa Shaikh1, Minh Trang Hoang2

  • 1Ministry of Health, New South Wales, Australia.

Studies in Health Technology and Informatics
|August 8, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a scalable, serverless machine learning operations (ML Ops) architecture for rapid sepsis detection in emergency departments. The system achieved 99.7% HL7 message processing, demonstrating effective near real-time clinical decision support.

Keywords:
Sepsisartificial intelligenceemergency departmentmachine learningserverless cloudsystem architecture

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Area of Science:

  • Clinical Informatics
  • Machine Learning Operations (ML Ops)
  • Healthcare Systems Engineering

Background:

  • Sepsis detection in Emergency Departments (ED) requires timely analysis of patient data.
  • Existing systems may face challenges with scalability and real-time processing.
  • Machine learning operations (ML Ops) offer a framework for efficient model deployment and management.

Purpose of the Study:

  • To present a scalable, serverless ML Ops architecture for near real-time sepsis detection in ED waiting rooms.
  • To detail the implementation of this architecture using cloud-based services.
  • To evaluate the system's performance in processing healthcare messages.

Main Methods:

  • Developed a serverless architecture on Amazon Web Services (AWS).
  • Utilized MuleSoft for Health Level 7 (HL7) message processing.
  • Employed AWS Lambda for data handling and AWS SageMaker for model deployment.
  • Stored data in Aurora PostgreSQL and visualized results using Tableau™.

Main Results:

  • Achieved 99.7% successful processing rate for HL7 messages.
  • Demonstrated a scalable and robust system for near real-time data analysis.
  • Identified areas for optimization including system downtime and peak execution times.

Conclusions:

  • The proposed serverless ML Ops architecture is effective for near real-time sepsis detection in EDs.
  • The system shows high message processing efficiency, supporting clinical decision-making.
  • Further optimization is needed to address occasional downtime and performance bottlenecks.