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The discharge summary is crucial as it enables a smooth transition from a healthcare facility to a patient's home or another care setting. This critical document facilitates seamless continuity of care, ensuring patients receive the necessary support and attention.
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Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department - A Practical Guide
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Machine learning decision support model for discharge planning in stroke patients.

Yanli Cui1,2, Lijun Xiang1, Peng Zhao1,2

  • 1Department of Neurology, Nanfang Hospital, Southern Medical University, Guangzhou, China.

Journal of Clinical Nursing
|February 15, 2024
PubMed
Summary

Machine learning models can predict stroke patient discharge needs early. Key factors include NIHSS score, income, and frailty, aiding timely clinical decisions for better patient care.

Keywords:
decision supportdischarge dispositiondischarge planningmachine learningmedical decision‐makingpredictive factorsprospective studystroke

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Area of Science:

  • Neurology
  • Medical Informatics
  • Health Services Research

Background:

  • Efficient discharge planning for stroke patients is critical for recovery and resource management.
  • Early identification of patients requiring non-home discharge is challenging but essential for optimal care transitions.

Purpose of the Study:

  • To develop early predictive models for stroke patient discharge disposition using data available within 24 hours of admission.
  • To identify key patient characteristics and variables influencing discharge planning.

Main Methods:

  • A prospective observational study involving 523 stroke patients at a university hospital.
  • Development and evaluation of six machine learning models to predict home versus non-home discharge.
  • Analysis of feature importance to identify significant predictors.

Main Results:

  • The best-performing machine learning model achieved an AUC of 0.95 for predicting non-home discharge.
  • Top predictors included National Institutes of Health Stroke Scale (NIHSS) score, family income, Barthel index (BI) score, FRAIL score, fall risk, pressure injury risk, feeding method, depression, age, and dysphagia.
  • 30.01% of stroke patients had a non-home discharge.

Conclusions:

  • Machine learning models can effectively predict the need for non-home discharge in stroke patients.
  • Factors such as higher NIHSS, BI, FRAIL scores, family income, fall risk, pressure injury risk, older age, tube feeding, depression, and dysphagia are strong predictors.
  • These models support timely clinical decision-making and can improve discharge processes for stroke survivors.