Hunt, Christy L.

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.

Abstract Introduction: The inconsistent use of standardized approaches for classifying postamputation pain (PAP) has been a barrier to establishing its prevalence. Objectives: The primary objective of this systematic review and meta-analysis is to determine the prevalence of nontraumatic lower-extremity PAP using an established taxonomy. The secondary objective is to determine the prevalence of PAP subtypes, including phantom limb pain and residual limb pain (RLP). Methods: An a priori protocol was registered, and a database search was conducted by a reference librarian. Randomized trials and uncontrolled studies were eligible for inclusion. The risk of bias was assessed using a tool developed for uncontrolled studies. A total of 2679 studies were screened, and 13 studies met inclusion criteria (n = 1063). Results: The sources of risk of bias included selection bias and, to a lesser extent, whether the outcome was adequately ascertained. Two studies reported the prevalence of PAP and the pooled prevalence was 61% (95% confidence interval [CI], 33%–86%) with high heterogeneity (I2 = 93%). Thirteen studies reported the prevalence of phantom limb pain and the pooled prevalence was 53% (95% CI, 40%–66%) with high heterogeneity (I2 = 93%). Eight studies reported the prevalence of RLP and the pooled prevalence was 32% (95% CI 24%–41%) with high heterogeneity (I2 = 76%). Clinical subtypes of RLP were not reported. Conclusions: The prevalence of PAP is high in patients with nontraumatic lower-extremity amputations. Ongoing research that uses a taxonomy for PAP is needed to fully delineate the prevalence of PAP subtypes.