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Related Concept Videos

Patient-centered Care01:13

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Patient-centered care involves delivering care beyond inpatient hospitalization. Reflective practice can enhance a patient-centered approach. Reflective practice is a process of reasoning that considers all aspects of the present situation, including practicalities, learning from personal practice, and consideration of patient needs. Patients appreciate care decisions made while considering their input. Involving the patient in their care provides the patient with a sense of contribution rather...
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Related Experiment Video

Updated: May 3, 2026

E-Patient Counseling Trial E-PACO: Computer Based Education versus Nurse Counseling for Patients to Prepare for Colonoscopy
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Patient preference analysis for online consultation based on user-generated content.

Huchang Liao1, Yuan Zheng1, Xingli Wu1

  • 1Business School, Sichuan University, Chengdu 610064, China.

Artificial Intelligence in Medicine
|May 1, 2026
PubMed
Summary

Personalized online doctor recommendations are improved by analyzing patient preferences from reviews. This method addresses sparse data by disaggregating patient preferences, enhancing the online medical consultation experience.

Keywords:
Doctor selectionInformation reliabilityOnline consultationPreference disaggregation analysisUser-generated content

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Area of Science:

  • Computational linguistics
  • Health informatics
  • Artificial intelligence in healthcare

Background:

  • Online consultation platforms aim to personalize doctor recommendations.
  • Challenges exist in capturing patient preferences due to sparse medical records.
  • Existing methods struggle with individual-specific doctor attribute preferences.

Purpose of the Study:

  • To propose a patient preference disaggregation analysis method for personalized doctor recommendations.
  • To develop patient preference models using user-generated content.
  • To enhance the patient medical experience on online platforms.

Main Methods:

  • Utilized a compensatory value function to model patient preferences for doctor attributes.
  • Developed an optimization model with regularization to learn preference parameters from historical decisions.
  • Incorporated sentiment analysis of online reviews for doctor attribute performance (reliability, popularity).
  • Segmented patients into cohorts (e.g., gender, severity) and developed cohort-specific preference models.
  • Employed an individual-cohort matching model for tailored recommendations.

Main Results:

  • Validated the method on the Dxy.com platform, confirming distinct attribute preferences across patient cohorts.
  • Demonstrated that personalized recommendations significantly enhance the patient medical experience.
  • Showcased the effectiveness of preference disaggregation in overcoming data sparsity issues.

Conclusions:

  • Patient preference disaggregation analysis is effective for personalized doctor recommendations.
  • Cohort-based preference modeling captures diverse patient needs.
  • The proposed method improves patient satisfaction and experience in online healthcare settings.