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Related Experiment Videos

A Tool for Virtual Nursing Utilization in Emergency Reception: An Exploratory Machine Learning Approach.

Zhaoqiang Zhou1, John Geracitano1, Sandy Hatoum1

  • 1University of North Carolina at Chapel Hill.

Studies in Health Technology and Informatics
|May 23, 2026
PubMed
Summary

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Nursing Clinical Information System01:27

Nursing Clinical Information System

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Critical attributes of NCIS include:
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Steps in Outbreak Investigation

In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:

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This study created a machine learning tool to identify patients who would benefit most from virtual nursing (VN) assessments, aiming to improve hospital efficiency and patient care. The predictive scoresheet helps optimize resource allocation by selecting patients likely to have a shorter hospital length of stay (LOS).

Area of Science:

  • Health Informatics
  • Machine Learning in Healthcare
  • Nursing Administration

Background:

  • Virtual nursing (VN) is an emerging scalable solution for supporting hospital operations.
  • Efficiently identifying suitable patients for VN admission assessments is crucial for optimizing care delivery.
  • Predictive modeling can aid in patient selection for VN services.

Purpose of the Study:

  • To develop an exploratory predictive scoresheet for recommending patients for virtual nursing (VN) admission assessments.
  • To identify patient characteristics associated with a shorter hospital length of stay (LOS) suitable for VN.
  • To create a tool for improving triage decisions and resource allocation in healthcare settings.

Main Methods:

  • Utilized machine learning, specifically an XGBoost classifier, to analyze data from nine hospitals.
Keywords:
Health InformaticsHealth SystemMachine LearningVirtual Nursing

Related Experiment Videos

  • Incorporated patient, clinical, and hospital characteristics to predict short LOS (below median).
  • Developed a prototype scoresheet based on the top 20 predictive features identified by the model.

    • Main Results:

    • The XGBoost model demonstrated strong predictive performance with an AUC-ROC of 0.85.
    • The model achieved an F1 score of 0.78 for identifying patients with a short LOS.
    • Validation confirmed the scoresheet's effectiveness in identifying patients likely to benefit from VN assessments.

    Conclusions:

    • The developed predictive scoresheet is an effective tool for identifying patients suitable for virtual nursing (VN) admission assessments.
    • This tool can significantly improve triage decisions and optimize resource allocation within hospitals.
    • Machine learning-driven patient selection enhances the scalability and efficiency of virtual nursing programs.