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Triaging Patient Complaints: Monte Carlo Cross-Validation of Six Machine Learning Classifiers.

Adel Elmessiry1, William O Cooper2, Thomas F Catron2

  • 1North Carolina State University, Department of Computer Science, Raleigh, NC, United States.

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|August 2, 2017
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Summary

Automated analysis of patient complaints using machine learning can effectively identify issues requiring physician attention. This approach enhances healthcare service recovery and promotes physician self-regulation.

Keywords:
NLPmachine learningnatural language processingpatient complaints

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Area of Science:

  • Health Informatics
  • Natural Language Processing
  • Machine Learning

Background:

  • Unsolicited patient complaints are valuable for healthcare service recovery.
  • Manual processing of complaints is costly, slow, and difficult to scale.
  • Identifying complaints needing physician action is crucial for healthcare organizations.

Purpose of the Study:

  • To evaluate automatic patient triage for improved response times and scalability.
  • To enhance physician self-regulation through efficient complaint management.
  • To assess machine learning classifiers for classifying patient complaints.

Main Methods:

  • Compared machine learning classifiers to detect physician-associated complaints.
  • Utilized a dataset of 14,335 patient complaints linked to 768 physicians.
  • Validated results using 10-splits Monte Carlo cross-validation.

Main Results:

  • Achieved 82% accuracy and 81% F-score in complaint classification.
  • Demonstrated sensitivity of 0.76 and specificity of 0.87.
  • Successfully identified patient complaints requiring physician action.

Conclusions:

  • Natural language processing effectively models patient complaint text.
  • Automated methods can identify complaints needing physician intervention.
  • This technology supports improved healthcare service recovery and physician oversight.