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Patient Triage by Topic Modeling of Referral Letters: Feasibility Study.

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This summary is machine-generated.

This study shows that natural language processing and machine learning can automate patient triage for musculoskeletal conditions using referral letters. This approach effectively predicts treatment and aids in prioritizing patient care.

Keywords:
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Area of Science:

  • Health Informatics
  • Artificial Intelligence in Medicine
  • Clinical Decision Support Systems

Background:

  • Musculoskeletal conditions require timely management, often involving referrals from primary to secondary care.
  • Referral letters contain crucial information for decision-making but are underutilized for treatment prioritization.
  • Optimizing care pathways is essential due to increasing healthcare demands.

Purpose of the Study:

  • To explore the feasibility of automating patient triage for musculoskeletal conditions using natural language processing (NLP) and machine learning (ML).
  • To determine if referral letters can be automatically categorized into clinically relevant topics for treatment prediction.
  • To assess if clinicians can interpret these topics as meaningful patient cohorts.

Main Methods:

  • Latent Dirichlet Allocation (LDA) was used to model referral letters into topics.
  • A binary classifier was trained using extracted topics to predict treatment outcomes.
  • Qualitative evaluation assessed the clinical interpretability of the generated topics.

Main Results:

  • Machine learning classifiers significantly outperformed random chance in predicting treatment.
  • Topic modeling demonstrated effectiveness in supporting automated patient triage.
  • Qualitative analysis confirmed the clinical relevance and interpretability of the identified topics.

Conclusions:

  • Automated triage of patients with knee or hip pain is feasible using NLP and ML on referral letters.
  • This technology can streamline care pathways and improve the efficiency of specialist referrals.
  • The study highlights the potential of AI in optimizing musculoskeletal condition management.