A novel method of adverse event detection can accurately identify venous thromboembolisms (VTEs) from narrative electronic health record data

Rochefort, Christian M.; Verma, Aman; Eguale, Tewodros; Lee, Todd C.; Buckeridge, David L.

doi:10.1136/amiajnl-2014-002768

Cited by 63 publications

(75 citation statements)

References 41 publications

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“…The statistical NLP model predicting DVT achieved sensitivity of 0.80 (95% CI: 0.76-0.85), specificity of 0.98 (98% CI: 0.97-0.99) and positive predictive value (PPV) of 0.89 (95% CI: 0.85-0.93). As for the statistical NLP model predicting PE, sensitivity was 0.79 (95% CI: 0.73-0.85), specificity 0.99 (95% CI: 0.98-0.99), and PPV was 0.84 (95% CI: 0.75-0.92) [31].…”

Section: Literature Review Pilot Work: Novel Methods Of Ae Detectionmentioning

confidence: 97%

“…The results of preliminary studies that have implemented these NLP techniques are encouraging [29,30]. For instance, we recently conducted a pilot study to validate the accuracy of using statistical NLP for identifying cases of deep vein thrombosis (DVT) and pulmonary embolism (PE) from free-text electronic narrative radiology reports [31]. This method was found to be highly effective and accurate.…”

Section: Literature Review Pilot Work: Novel Methods Of Ae Detectionmentioning

confidence: 99%

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Accuracy of using automated methods for detecting adverse events from electronic health record data: a research protocol

2015

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BackgroundAdverse events are associated with significant morbidity, mortality and cost in hospitalized patients. Measuring adverse events is necessary for quality improvement, but current detection methods are inaccurate, untimely and expensive. The advent of electronic health records and the development of automated methods for encoding and classifying electronic narrative data, such as natural language processing, offer an opportunity to identify potentially better methods. The objective of this study is to determine the accuracy of using automated methods for detecting three highly prevalent adverse events: a) hospital-acquired pneumonia, b) catheter-associated bloodstream infections, and c) in-hospital falls.Methods/designThis validation study will be conducted at two large Canadian academic health centres: the McGill University Health Centre (MUHC) and The Ottawa Hospital (TOH). The study population consists of all medical, surgical and intensive care unit patients admitted to these centres between 2008 and 2014. An automated detection algorithm will be developed and validated for each of the three adverse events using electronic data extracted from multiple clinical databases. A random sample of MUHC patients will be used to develop the automated detection algorithms (cohort 1, development set). The accuracy of these algorithms will be assessed using chart review as the reference standard. Then, receiver operating characteristic curves will be used to identify optimal cut points for each of the data sources. Multivariate logistic regression and the areas under curve (AUC) will be used to identify the optimal combination of data sources that maximize the accuracy of adverse event detection. The most accurate algorithms will then be validated on a second random sample of MUHC patients (cohort 1, validation set), and accuracy will be measured using chart review as the reference standard. The most accurate algorithms validated at the MUHC will then be applied to TOH data (cohort 2), and their accuracy will be assessed using a reference standard assessment of the medical chart.DiscussionThere is a need for more accurate, timely and efficient measures of adverse events in acute care hospitals. This is a critical requirement for evaluating the effectiveness of preventive interventions and for tracking progress in patient safety through time.

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Section: Literature Review Pilot Work: Novel Methods Of Ae Detectionmentioning

confidence: 97%

Section: Literature Review Pilot Work: Novel Methods Of Ae Detectionmentioning

confidence: 99%

Accuracy of using automated methods for detecting adverse events from electronic health record data: a research protocol

2015

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“…History of VTE will be determined by applying our statistical NLP models to narrative reports of radiological examinations performed since 2004 [39]. (b) Anticoagulant drug use will be assessed as a time-varying covariate updated every day and representing current use (yes or no).…”

Section: Methodsmentioning

confidence: 99%

Improving patient safety by optimizing the use of nursing human resources

2015

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BackgroundRecent ecological studies have suggested that inadequate nurse staffing may contribute to the incidence of adverse events in acute care hospitals. However, longitudinal studies are needed to further examine these associations and to identify the staffing patterns that are of greatest risk. The aims of this study are to determine if (a) nurse staffing levels are associated with an increased risk of adverse events, (b) the risk of adverse events in relationship to nurse staffing levels is modified by the complexity of patient requirements, and (c) optimal nurse staffing levels can be established.Methods/designA dynamic cohort of all adult medical, surgical, and intensive care unit patients admitted between 2010 and 2015 to a Canadian academic health center will be followed during the inpatient and 7-day post-discharge period to assess the occurrence and frequency of adverse events in relationship to antecedent nurse staffing levels. Four potentially preventable adverse events will be measured: (a) hospital-acquired pneumonia, (b) ventilator-associated pneumonia, (c) venous thromboembolism, and (d) in-hospital fall. These events were selected for their high incidence, morbidity and mortality rates, and because they are hypothesized to be related to nurse staffing levels. Adverse events will be ascertained from electronic health record data using validated automated detection algorithms. Patient exposure to nurse staffing will be measured on every shift of the hospitalization using electronic payroll records. To examine the association between nurse staffing levels and the risk of adverse events, four Cox proportional hazards regression models will be used (one for each adverse event), while adjusting for patient characteristics and risk factors of adverse event occurrence. To determine if the association between nurse staffing levels and the occurrence of adverse events is modified by the complexity of patient requirements, interaction terms will be included in the regression models, and their significance assessed. To assess for the presence of optimal nurse staffing levels, flexible nonlinear spline functions will be fitted.DiscussionThis study will likely generate evidence-based information that will assist managers in making the most effective use of scarce nursing resources and in identifying staffing patterns that minimize the risk of adverse events.Electronic supplementary materialThe online version of this article (doi:10.1186/s13012-015-0278-1) contains supplementary material, which is available to authorized users.

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“…Rochefort et al (2015) developed document classifiers to classify whether a clinical note contains deep venous thromboembolisms and pulmonary embolism. Haerian et al (2012) applied distance supervision to identify terms (e.g., including “suicidal”, “self harm”, and “diphenhydramine overdose”) associated with suicide events.…”

Section: Related Workmentioning

confidence: 99%

Bidirectional RNN for Medical Event Detection in Electronic Health Records

Jagannatha

2016

Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

218

128

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Sequence labeling for extraction of medical events and their attributes from unstructured text in Electronic Health Record (EHR) notes is a key step towards semantic understanding of EHRs. It has important applications in health informatics including pharmacovigilance and drug surveillance. The state of the art supervised machine learning models in this domain are based on Conditional Random Fields (CRFs) with features calculated from fixed context windows. In this application, we explored recurrent neural network frameworks 1 and show that they significantly out-performed the CRF models.

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A novel method of adverse event detection can accurately identify venous thromboembolisms (VTEs) from narrative electronic health record data

Cited by 63 publications

References 41 publications

Accuracy of using automated methods for detecting adverse events from electronic health record data: a research protocol

Accuracy of using automated methods for detecting adverse events from electronic health record data: a research protocol

Improving patient safety by optimizing the use of nursing human resources

Bidirectional RNN for Medical Event Detection in Electronic Health Records

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