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Updated: Sep 6, 2025

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Predictive Analysis of Hospital HIS System Usage Satisfaction Based on Machine Learning.

Yuhang Hu1, Haotian Gan2

  • 1Finance Section, The Second Affiliated Hospital of Qiqihar Medical University, Qiqihar, 161006 Heilongjiang, China.

Computational and Mathematical Methods in Medicine
|June 24, 2022
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Summary

This study introduces a machine learning (ML) model to predict hospital information system (HIS) user satisfaction. The ML approach, utilizing AdaBoost with Support Vector Machines (SVM), achieves over 95% accuracy, enhancing HIS effectiveness.

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Area of Science:

  • Health Informatics
  • Machine Learning Applications in Healthcare
  • Information Systems Management

Background:

  • Hospital Information Systems (HIS) offer significant benefits, including cost reduction, efficiency improvement, and error minimization in medical services.
  • A key challenge in HIS implementation is ensuring effective utilization by medical staff, impacting overall system success.
  • Predicting and analyzing user satisfaction is crucial for optimizing HIS adoption and performance.

Purpose of the Study:

  • To develop and evaluate a machine learning (ML) model for predicting user satisfaction with Hospital Information Systems (HIS).
  • To enhance the generalization ability and accuracy of ML models for HIS satisfaction prediction.
  • To provide a theoretical basis and practical strategy for HIS development and effective implementation.

Main Methods:

  • Literature review on HIS development trends and related technologies.
  • Application of machine learning algorithms, specifically Support Vector Machine (SVM) enhanced with AdaBoost technique.
  • Inclusion of a diversity metric to improve the performance of basic learners within the AdaBoost algorithm.

Main Results:

  • The proposed ML model, combining SVM and AdaBoost with a diversity metric, demonstrated high predictive accuracy.
  • Achieved accuracy rates exceeding 95% on a self-built dataset, even with small data volumes.
  • The study validates the superiority of the proposed model for predicting HIS user satisfaction.

Conclusions:

  • Machine learning, particularly the AdaBoost-enhanced SVM model, provides an effective strategy for predicting HIS user satisfaction.
  • Optimizing ML model performance through techniques like diversity metrics is key to successful HIS adoption.
  • The findings support the development of more user-centric and effective HIS implementations in healthcare settings.