Extracting Follow-Up Recommendations and Associated Anatomy from Radiology Reports

摘要：

　　Adherence rates for timely imaging follow-up are usually low due to low rates of diligence by referring physicians and/or patients with following recommendations for follow-up imaging.This can lead to delayed treatment,poor patient outcomes,unnecessary testing,and legal liability.Existing follow-up recommendation detection methods are often disease-and modality-specific.To address some of these limitations,we present a generic radiology report processing pipeline that can be used to extract follow-up imaging recommendations by anatomy using an ontology-based approach.Using a large dataset from three hospitals,we discuss our methodology in the context of identifying followup imaging recommendations that are related to lung,adrenal and/or thyroid conditions.The algorithm has 99%accuracy(95%CI: 95.8-99%).We also present an interactive dashboard that can be used to understand trends related to follow-up recommendations.

关键词： Medical Informatics Applications Follow-Up Studies Quality Assurance,Health Care

作者: Thusitha Mabotuwana Christopher S Hall Sandeep Dalal Joel Tieder Martin L.Gunn

作者单位: Philips Healthcare,Seattle,WA,USA Philips Research,Cambridge,MA,USA University of Washington,Seattle,WA,USA

会议类型: 国际会议

会议名称: 第十六届世界医药健康信息学大会((MEDINFO2017)、第二届世界医药健康信息学华语论坛(WCHIS 2017)、第15届全国医药信息学大会(CMIA 2017)

会议地点: 苏州

会议语种:英文

页码: 1090-1094

在线出版日期: 2017-08-21（万方平台首次上网日期，不代表论文的发表时间）