Automated Detection of Postoperative Surgical Site Infections Using Supervised Methods with Electronic Health Record Data.
The National Surgical Quality Improvement Project (NSQIP) is widely recognized as "the best in the nation" surgical quality improvement resource in the United States. In particular, it rigorously defines postoperative morbidity outcomes, including surgical adverse events occurring within 30 days of surgery. Due to its manual yet expensive construction process, the NSQIP registry is of exceptionally high quality, but its high cost remains a significant bottleneck to NSQIP's wider dissemination. In this work, we propose an automated surgical adverse events detection tool, aimed at accelerating the process of extracting postoperative outcomes from medical charts. As a prototype system, we combined local EHR data with the NSQIP gold standard outcomes and developed machine learned models to retrospectively detect Surgical Site Infections (SSI), a particular family of adverse events that NSQIP extracts. The built models have high specificity (from 0.788 to 0.988) as well as very high negative predictive values (&gt;0.98), reliably eliminating the vast majority of patients without SSI, thereby significantly reducing the NSQIP extractors' burden.
Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA.
Zhen
Z
Hu
Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA.
Gyorgy J
GJ
Simon
Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA.
Elliot G
EG
Arsoniadis
Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA.
Yan
Y
Wang
Department of Surgery, University of Minnesota, Minneapolis, MN, USA.
Mary R
MR
Kwaan
Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA.
Genevieve B
GB
Melton
D057225:Data Mining / Q000379:methods; D020000:Decision Support Systems, Clinical / Q000458:organization & administration*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D057286:Electronic Health Records / Q000706:statistics & numerical data*; D006801:Humans; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D000069553:Supervised Machine Learning; D013530:Surgical Wound Infection / Q000175:diagnosis*