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Evaluating Doctor Performance: Ordinal Regression-Based Approach.

Yong Shi1,2,3,4, Peijia Li5,6, Xiaodan Yu7

  • 1School of Economics and Management, University of Chinese Academy of Sciences, Beijing, China.

Journal of Medical Internet Research
|July 20, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a machine learning model for automatic doctor performance evaluation in mobile health (mHealth), addressing the lack of patient feedback. The novel approach improves accuracy in assessing online consultations, guiding platform development.

Keywords:
mHealthordinal partitioningordinal regressionperformance evaluationsupport vector machines

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Health Informatics

Background:

  • Doctor performance evaluation is crucial for mobile health (mHealth) quality and patient satisfaction.
  • A significant challenge in mHealth is the low rate of patient-provided performance evaluations.
  • Automatic evaluation models are needed to address the lack of explicit feedback in online consultations.

Purpose of the Study:

  • To develop an automatic doctor performance evaluation model using online textual consultations.
  • To address the challenge of missing patient ratings in mHealth platforms.
  • To improve the accuracy and reliability of physician performance assessments in digital health.

Main Methods:

  • Modeling doctor performance evaluation as an ordinal regression problem.
  • Utilizing a Support Vector Machine with an Ordinal Partitioning model (SVMOP).
  • Incorporating customized text features and Gradient Boosting Decision Tree for enhanced prediction.

Main Results:

  • The SVMOP model demonstrated improved performance over existing auto-evaluation methods.
  • Significant reductions in Mean Absolute Error (MAE) and Mean Squared Error (MSE) were achieved.
  • Enhanced pairwise accuracy (PAcc) indicates a more reliable evaluation of doctor performance.

Conclusions:

  • The proposed ordinal regression model effectively automates doctor performance evaluation in mHealth.
  • The model provides insights into predictive features, aiding in physician guidance and mHealth platform development.
  • This approach offers a scalable solution for quality assessment in digital healthcare interactions.