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Related Concept Videos

Analgesia and Pain Management01:25

Analgesia and Pain Management

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Pain is critical to various clinical pathologies, provoking an urgent need for effective management. Pain, whether acute or chronic, is a complex neurochemical process. Its alleviation depends on the type, with nonopioid analgesics effective for mild to moderate pain, such as musculoskeletal or inflammatory pain, while neuropathic pain responds best to anticonvulsants, tricyclic antidepressants, or serotonin/norepinephrine reuptake inhibitors. For severe acute or chronic pain, opioids may be...
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Related Experiment Video

Updated: Jan 8, 2026

Determining Pain Detection and Tolerance Thresholds Using an Integrated, Multi-Modal Pain Task Battery
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Determining Pain Detection and Tolerance Thresholds Using an Integrated, Multi-Modal Pain Task Battery

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A Rule-Based Automated Triage Model Using Natural Language Processing for Pain Medicine-Development and

Lan Jiang1, Yu-Li Huang1, Jungwei Fan2

  • 1Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, Minnesota, United States.

Applied Clinical Informatics
|December 18, 2025
PubMed
Summary
This summary is machine-generated.

A new automated referral triage system for pain medicine uses Natural Language Processing (NLP) and post-processing rules to improve patient scheduling accuracy. This knowledge-driven approach significantly outperforms machine learning models, enhancing efficiency and resource allocation.

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Area of Science:

  • Medical Informatics
  • Clinical Workflow Optimization
  • Natural Language Processing in Healthcare

Background:

  • Standardized triage protocols are lacking in pain medicine, leading to care delays and resource inefficiency.
  • Automated systems can streamline patient scheduling and improve access to appropriate pain management treatments.

Purpose of the Study:

  • To develop a rule-based automated referral triage system for pain medicine using patient medical notes.
  • To enhance the accuracy and efficiency of patient scheduling to specific pain medicine procedures.

Main Methods:

  • Developed a rule-based system leveraging Natural Language Processing (NLP) and post-processing rules derived from clinical expertise.
  • Iteratively refined NLP and post-processing rules through review of 76 patient cases.
  • Integrated the system into an electronic health record (EHR) platform for real-time application and incorporated a post-processing regression model.

Main Results:

  • The NLP and post-processing rules improved accuracy from 76.3% to 80.3% after three iterations, outperforming preliminary machine learning (ML) approaches.
  • The post-processing model further increased accuracy to 84.2%.
  • Real-world implementation achieved 83.5% accuracy, showing a statistically significant improvement over ML models (p=0.018).

Conclusions:

  • A knowledge-driven, automated triage system is feasible and beneficial for pain medicine referral processes.
  • This approach offers a foundation for developing similar triaging solutions in other medical specialties.
  • The system enhances scheduling workflows and usability in clinical settings.