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Related Experiment Video

Updated: May 29, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

An AI-Enabled Predictive Harm Response Management Algorithmic Tool to Reduce Adverse Events in Health Care Settings

Marion Eckert1, Greg Sharplin1, Wolfgang Mayer2

  • 1Rosemary Bryant AO Research Centre, College of Health, Adelaide University, Adelaide, South Australia, Australia.

JMIR Research Protocols
|May 27, 2026
PubMed
Summary

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This summary is machine-generated.

This study develops a predictive harm algorithm using Australian healthcare data to help nurses and midwives proactively prevent adverse events. Real-time insights from this tool will improve patient safety and resource allocation.

Area of Science:

  • Healthcare Analytics
  • Clinical Risk Management
  • Predictive Modeling in Healthcare

Background:

  • Current healthcare data analysis methods are retrospective, limiting real-time insights for nurses and midwives.
  • Predictive analytics using administrative data can identify harm risk profiles for proactive care.
  • A proof-of-concept predictive risk algorithm will be developed to support clinical decision-making.

Purpose of the Study:

  • Identify clinical harm outcomes and relevant data sources within South Australian health networks.
  • Develop a data solution to model harm risk predictors.
  • Identify actionable factors influencing clinical harm for intervention.

Main Methods:

  • The study involves three phases: model generation, evaluation, and prototype development.
Keywords:
AIartificial intelligencecode blackmedication errormidwiferynursingpatient fallsrisk predictionviolence and aggression

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  • Data linkage and analysis will be conducted following ethical and governance approvals.
  • Iterative model development using training, validation, and test datasets, with exploratory data analysis and feature selection.

    • Main Results:

    • The study commenced in July 2021 and is expected to conclude by December 2025.
    • Finalized results, including the developed predictive algorithm, are anticipated in December 2025.
    • An interactive dashboard will be developed to display real-time risk insights.

    Conclusions:

    • The research aims to develop a transferable algorithm for healthcare environments to reduce adverse events.
    • Publicly available reports and peer-reviewed manuscripts will detail research activities and findings.
    • The predictive harm algorithm is expected to enhance patient safety and optimize resource management.