Jiang Lan, Huang Yu-Li, Fan Jungwei, Hunt Christy L, Eldrige Jason S
Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, 55905, USA.
Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, 55905, USA.
J Med Syst. 2025 Jan 21;49(1):14. doi: 10.1007/s10916-025-02148-5.
Effective referral triaging enhances patient service outcomes, experience and access to care especially for specialized procedures. This study presents the development and implementation of an automated triaging system to predict patients who would benefit from Spinal Cord Stimulation (SCS) procedure for their pain management. The proposed triage system aims to improve the triage process by reducing unnecessary appointments before SCS assessment, ensuring appropriate pain management care. It compares various machine learning techniques for the prediction while addressing the class imbalance and overlap challenges inherent in the data. Both data-level and algorithm-level approaches were explored. Two years of patient data was collected including patient characteristics, diagnosis history, pain symptoms, appointment history, medication history, and concepts from clinical notes extracted using Natural Language Processing. EasyEnsemble with Ada Boosting method, an algorithm-level approach, showed the most promising results. The tenfold validation indicated the average area under curve of 0.82, true positive rate (TPR) of 77.3%, and true negative rate (TNR) of 73.0%. The probability threshold was adjusted to 0.575 to meet practice expectation of 15% or less on false positive rate (FPR). The implementation pipeline for the selected model was designed to be applicable to real clinical settings. The one-year implementation results showed TPR of 64.7% and TNR of 87.2%, which reduced FPR by 12.8% while reduced TPR by 12.6%. The trade-off was acceptable to practice. The proposed triage system demonstrated promising accuracy, leading to the enhancement of scheduling systems, patient care, and the reduction of unnecessary appointments in a pain medicine setting.
有效的转诊分诊可提高患者服务效果、改善就医体验并增加获得医疗服务的机会，尤其是对于专科手术而言。本研究介绍了一种自动分诊系统的开发与实施，该系统用于预测能从脊髓刺激（SCS）手术中受益以进行疼痛管理的患者。所提出的分诊系统旨在通过减少SCS评估前不必要的预约来改善分诊流程，确保提供适当的疼痛管理护理。它在解决数据中固有的类别不平衡和重叠挑战的同时，比较了各种机器学习技术用于预测。探索了数据层面和算法层面的方法。收集了两年的患者数据，包括患者特征、诊断史、疼痛症状、预约史、用药史以及使用自然语言处理从临床记录中提取的概念。采用Ada Boosting方法的EasyEnsemble（一种算法层面的方法）显示出最有前景的结果。十折交叉验证表明曲线下面积平均为0.82，真阳性率（TPR）为77.3%，真阴性率（TNR）为73.0%。将概率阈值调整为0.575，以使假阳性率（FPR）符合15%或更低的实际预期。所选模型的实施流程设计为适用于实际临床环境。一年的实施结果显示TPR为64.7%，TNR为87.2%，FPR降低了12.8%，而TPR降低了12.6%。这种权衡在实际应用中是可以接受的。所提出的分诊系统显示出有前景的准确性，从而改善了排班系统、患者护理，并减少了疼痛医学环境中不必要的预约。
