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Using natural language processing of clinical text to enhance identification of opioid‐related overdoses in electronic health records data
Author(s) -
Hazlehurst Brian,
Green Carla A.,
Perrin Nancy A.,
Brandes John,
Carrell David S.,
Baer Andrew,
DeVeaughGeiss Angela,
Coplan Paul M.
Publication year - 2019
Publication title -
pharmacoepidemiology and drug safety
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.023
H-Index - 96
eISSN - 1099-1557
pISSN - 1053-8569
DOI - 10.1002/pds.4810
Subject(s) - medicine , drug overdose , opioid overdose , heroin , gold standard (test) , substance abuse , opioid , medical emergency , poison control , drug , psychiatry , (+) naloxone , receptor
Purpose To enhance automated methods for accurately identifying opioid‐related overdoses and classifying types of overdose using electronic health record (EHR) databases. Methods We developed a natural language processing (NLP) software application to code clinical text documentation of overdose, including identification of intention for self‐harm, substances involved, substance abuse, and error in medication usage. Using datasets balanced with cases of suspected overdose and records of individuals at elevated risk for overdose, we developed and validated the application using Kaiser Permanente Northwest data, then tested portability of the application using Kaiser Permanente Washington data. Datasets were chart‐reviewed to provide a gold standard for comparison and evaluation of the automated method. Results The method performed well in identifying overdose (sensitivity = 0.80, specificity = 0.93), intentional overdose (sensitivity = 0.81, specificity = 0.98), and involvement of opioids (excluding heroin, sensitivity = 0.72, specificity = 0.96) and heroin (sensitivity = 0.84, specificity = 1.0). The method performed poorly at identifying adverse drug reactions and overdose due to patient error and fairly at identifying substance abuse in opioid‐related unintentional overdose (sensitivity = 0.67, specificity = 0.96). Evaluation using validation datasets yielded significant reductions, in specificity and negative predictive values only, for many classifications mentioned above. However, these measures remained above 0.80, thus, performance observed during development was largely maintained during validation. Similar results were obtained when evaluating portability, although there was a significant reduction in sensitivity for unintentional overdose that was attributed to missing text clinical notes in the database. Conclusions Methods that process text clinical notes show promise for improving accuracy and fidelity at identifying and classifying overdoses according to type using EHR data.