Abstract Pain medicine triage plays a crucial role in ensuring patients receive timely and appropriate care by scheduling them to the most suitable treatment path. However, the absence of standardized triage protocols in pain medicine often leads to inefficiencies, including delay of care and wastage of healthcare resources. This study aims to develop a rule-based automated referral triage system leveraging information from patients' medical notes for scheduling patients to specific procedures in the pain medicine department. The proposed triage system, grounded in the knowledge and expertise of clinical providers, processed referral order comments and referring provider notes by iteratively refining the Natural Language Processing (NLP) rules and post-processing rules through intensively reviewing 76 patients. A post-processing regression model was incorporated to further enhance the accuracy. To ensure alignment with real-world practices, the system was integrated into an electronic health record (EHR) platform for real-time application, streamlining scheduling workflows and enhancing usability in daily clinical settings. After three iterations, the proposed NLP and post-processing rules improved accuracy from 76.3 to 80.3% compared to machine learning (ML) approaches in the preliminary study. The post-processing model further increased accuracy to 84.2%. The implementation accuracy of 200 cases for the first 3 months was consistent with our prediction at 83.5%, which concluded that the improvement over ML models (p-value = 0.018) was statistically significant at 95% significance level. This study demonstrates the feasibility and benefits of a knowledge-driven approach to referral triage in specialized medical fields. It lays a foundation for others in building similar triaging solutions to other specialties.
Acetaminophen, autism, and analgesia in pregnancy—a pain medicine perspective
AbstractObjectiveThis case report presents an application of percutaneous peripheral nerve stimulation to the right superficial peroneal nerve to treat a patient with chronic intractable L5–S1 radiculopathy pain that conventional treatment failed to ameliorate.MethodsThe patient underwent an uneventful implantation of a percutaneous peripheral nerve stimulator. The implanted lead (15 cm in length and 1.2 mm in diameter) containing the receiver coil and 3 stimulation electrodes (Bioness Stimrouter®, Valencia, CA, U.S.A.) was implanted parallel with the trajectory of the right superficial peroneal nerve.ResultsTwo weeks after implantation of the percutaneous peripheral nerve stimulator, the patient experienced excellent pain relief and reported a significant increase in mobility. At the 3‐month follow‐up consultation, the patient reported maintenance of the reduction of pain in his right lower extremity as well as improved performance in his daily activities.ConclusionPercutaneous peripheral nerve stimulation offers an alternative treatment option for intractable pain associated with chronic radiculopathy, especially for patients in whom conventional treatment options have been exhausted. Further clinical series involving larger numbers of patients are warranted in order to assess the definitive role of percutaneous peripheral nerve stimulation for the treatment of chronic intractable radiculopathy pain.