Characterizing Surgical Site Infection Signals in Clinical Notes

摘要：

　　Surgical site infections(SSIs)are the most common and costly of hospital acquired infections.An important step in reducing SSIs is accurate SSI detection,which enables measurement and quality improvement,but currently remains expensive through manual chart review.Building off of previous work for automated and semi-automated SSI detection using expertderived strong features from clinical notes,we hypothesized that additional SSI phrases may be contained in clinical notes.We systematically characterized phrases and expressions associated with SSIs.While 83%of expert-derived original terms overlapped with new terms and modifiers,an additional 362 modifiers associated with both positive and negative SSI signals were identified and 62 new base observations and actions were identified.Clinical note queries with the most common base terms revealed another 49 modifiers.Clinical notes contain a wide variety of expressions describing infections occurring among surgical specialties which may provide value in improving the performance of SSI detection algorithms.

关键词： Surgical Wound Infection Quality and Safety Text-mining

作者: Steven J Skube Zhen Hu Elliot G Arsoniadis Gyorgy J Simon Elizabeth C Wick Clifford Y Ko Genevieve B Melton

作者单位: Department of Surgery,University of Minnesota,Minneapolis,MN,USA Institute for Health Informatics,University of Minnesota,Minneapolis,MN,USA Department of Surgery,University of Minnesota,Minneapolis,MN,USA;Institute for Health Informatics,Un Department of Surgery,University of California San Francisco,San Francisco,CA,USA Department of Surgery,University of California Los Angeles,Los Angeles,CA,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: 955-959

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）