Optimal Methods for Notifying Clinicians about Epilepsy Surgery Patients

项目摘要 癫痫是美国主要的神经疾病之一，影响着479,000多名儿童 以及超过200万的成年人。约30%的癫痫患者癫痫发作控制较差，尽管 抗癫痫药物，是神经外科干预的潜在候选者。及早识别和 转介可能接受手术的儿童是复杂的，虽然有相关的指南，但有 没有标准的流程来有效地识别那些符合神经外科干预标准的患者。 考虑到电子健康记录(EHR)中可用的大量基于笔记的数据，对 有效地保留和处理所有相关的患者信息。自然语言处理 (NLP)和机器学习技术已成功地用于评估临床笔记和制定 研究环境中的建议。然而，NLP技术很少集成到实践中来 提供实时临床决策支持。我们开发并回溯评估了NLP系统，以帮助 确定符合神经外科标准的患者，从而实现手术会诊和评估 更快地发生。了解到临床决策支持可以改善护理结果，我们提议的研究将 将NLP应用于临床实践，建立决策支持机制，缩短手术时间 对于符合条件的患者。 该项目的目标是将NLP直接实施到临床护理中，并确定最有效的 决策支持机制，为癫痫患者坚持手术咨询提供建议。长的- 该项目的长期目标是缩短难治性癫痫患者首次手术评估的时间。 将NLP分类标准与临床实践相结合。 该项目是该领域第一个研究将NLP建议整合到临床护理中的项目之一。我们 我将使用人为因素工程框架来设计和分析两种不同的警报方法 为了最适合临床工作流程，在减少警觉疲劳的同时，产生最佳的提供商遵从性。 癫痫的进展记录可以跨医院分类，如果成功，该系统将被实施 在美国各地的其他儿科机构。

Project Summary Epilepsy is one of the leading neurological disorders in the United States, affecting more than 479,000 children and over 2 million adults. Approximately 30% of epileptic patients have poor seizure control despite antiepileptic medications and are potential candidates for neurosurgical intervention. Early identification and referral of children who are potential surgical candidates is complex and while relevant guidelines exist, there is no standard process to efficiently identify those patients meeting criteria for neurosurgical intervention. Given the large corpus of note-based data available in the electronic health record (EHR), it is challenging for providers to efficiently retain and process all the pertinent patient information. Natural Language Processing (NLP) and machine learning techniques have been successfully used to evaluate clinical notes and make recommendations in the research setting. However, NLP techniques are rarely integrated into practice to provide real-time clinical decision support. We developed and retrospectively evaluated a NLP system to help identify those patients who meet neurosurgical criteria and therefore enable surgical consults and evaluations to occur sooner. Knowing clinical decision support can improve outcomes of care, our proposed research will implement NLP into clinical practice and develop a decision support mechanism to improve the time to surgery for eligible patients. The objective of this project is to implement NLP directly into clinical care and determine the most effective decision support mechanism for provider adherence to epilepsy surgical consult recommendations. The long- term goal of this project is to reduce the time to initial surgery evaluation for patients with intractable epilepsy by integrating NLP-classification criteria into clinical practice. This project is one of the first in the field to study the integration of NLP recommendations into clinical care. We will use a human factors engineering framework to design and to analyze two different alerting methodologies for the best-fit for clinical workflow to produce the optimum provider adherence while reducing alert fatigue. Epilepsy progress notes can be classified across hospitals, and if successful, the system will be implemented in additional pediatric institutions around the United States.