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Real-Time and Retrospective Health-Analytics-as-a-Service: A Novel Framework.

Hamzeh Khazaei1, Carolyn McGregor, J Mikael Eklund

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Summary
This summary is machine-generated.

This study presents a novel health analytics-as-a-service framework for critical care units. The Artemis-In-Cloud (Artemis-IC) project demonstrates real-time monitoring and retrospective analysis capabilities, optimizing resource allocation in neonatal intensive care units (NICUs).

Keywords:
analytics-as-a-servicebig data, health informaticscloud computingdecision support systemperformance modelingphysiological datapremature babiesreal-time analyticsretrospective analysis

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Area of Science:

  • Health Informatics
  • Cloud Computing
  • Data Analytics

Background:

  • Analytics-as-a-service (AaaS) offers significant benefits in health informatics for patients, providers, and governments.
  • This work introduces a novel approach to implement health analytics as services, specifically for critical care units.

Purpose of the Study:

  • To design, implement, evaluate, and deploy an extendable, big-data compatible framework for health-analytics-as-a-service.
  • The framework aims to provide both real-time and retrospective analysis capabilities for critical care data.

Main Methods:

  • A flexible and configurable framework utilizing modern data acquisition, transformation, storage, analytics, knowledge extraction, and visualization technologies.
  • Instantiation of the framework through the Artemis project for live monitoring and retrospective research in neonatal intensive care units (NICUs).
  • The Artemis-In-Cloud (Artemis-IC) project customizes the framework for NICU environments.

Main Results:

  • A pilot of the Artemis framework was deployed in the SickKids hospital NICU.
  • Performance measures for Artemis-IC deployment were predicted using pilot data, estimating resource needs (32 GB memory, 16 CPU cores, 8.6 TB storage/year for 36 beds).
  • Analysis indicated that increasing NICU capacity to 90 beds would accommodate all patients, requiring an estimated 16 TB storage/year, 55 GB memory, and 28 CPU cores.

Conclusions:

  • A cloud architecture for analyzing physiological data to support clinical decisions in tertiary care has been developed.
  • The study demonstrates how to size cloud equipment based on realistic patient data and required clinical decision support algorithms.
  • The proposed framework is replicable for any critical care setting within a tertiary institution.