IBM T.J. Watson Research Center, Yorktown Heights, NY.
AMIA Annu Symp Proc. 2022 Feb 21;2021:763-772. eCollection 2021.
Overabundance of information within electronic health records (EHRs) has resulted in a need for automated systems to mitigate the cognitive burden on physicians utilizing today's EHR systems. We present ProSPER, a Problem-oriented Summary of the Patient Electronic Record that displays a patient summary centered around an auto-generated problem list and disease-specific views for chronic conditions. ProSPER was developed using 1,500 longitudinal patient records from two large multi-specialty medical groups in the United States, and leverages multiple natural language processing (NLP) components targeting various fundamental (e.g. syntactic analysis), clinical (e.g. adverse drug event extraction) and summarizing (e.g. problem list generation) tasks. We report evaluation results for each component and discuss how specific components address existing physician challenges in reviewing EHR data. This work demonstrates the need to leverage holistic information in EHRs to build a comprehensive summarization application, and the potential for NLP-based applications to support physicians and improve clinical care.
电子健康记录 (EHR) 中的信息过多，导致需要自动化系统来减轻医生在使用当今 EHR 系统时的认知负担。我们提出了 ProSPER，这是一种针对患者电子记录的面向问题的摘要，它围绕自动生成的问题列表和慢性病的特定视图显示患者摘要。ProSPER 是使用来自美国两个大型多专业医疗集团的 1500 份纵向患者记录开发的，利用了多个自然语言处理 (NLP) 组件，针对各种基础（例如句法分析）、临床（例如不良药物事件提取）和总结（例如问题列表生成）任务。我们报告了每个组件的评估结果，并讨论了特定组件如何解决医生在审查 EHR 数据方面的现有挑战。这项工作表明需要利用 EHR 中的整体信息来构建全面的总结应用程序，以及基于 NLP 的应用程序在支持医生和改善临床护理方面的潜力。
