Identifying naloxone administrations in electronic health record data using a text-mining tool

Abstract

Background: Effective and efficient methods are needed to identify naloxone administrations within electronic health record (EHR) data to conduct overdose surveillance and research. The objective of this study was to develop and validate a text-mining tool to identify naloxone administrations in EHR data. Methods: Clinical notes stored in databases between January 2017 and March 2018 were used to iteratively develop a text-mining tool to identify naloxone administrations. The first iteration of the tool used broad search terms. Then, after reviewing clinical notes of overdose encounters, we developed a list of phrases that described naloxone administrations to inform iteration two. While validating iteration two, additional phrases were found, which were then added to inform the final iteration. The comparator was an administrative code query extracted from the EHR. Medical record review was used to identify true positives. The primary outcome was the positive predictive values (PPV) of the second iteration, final iteration, and administrative code query. Results: Iteration two, the final iteration, and the administrative code had PPVs of 84.3% (95% confidence interval [CI] 78.6–89.0%), 83.8% (95% CI 78.6–88.2%), and 57.1% (95% CI 47.1–66.8%), respectively. Both iterations of the tool had a significantly higher PPV than the administrative code (p < 0.001). Conclusions: A text-mining tool improved the identification of naloxone administrations in EHR data from less than 60% with the administrative code to greater than 80% with both versions of the tool. Text-mining tools can inform the use of more sophisticated informatics methods, which often require significant time, resource, and expertise investment.

Keywords:

• Naloxone
• electronic health record
• text mining
• clinical notes
• positive predictive value

Acknowledgements

The authors wish to acknowledge Kris Wain, who performed the data queries. The National Institutes of Health did not contribute to the study design, collection, analysis, or interpretation of data, writing the report, or the decision to submit this article for publication. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes for Health.

Additional information

Funding

Research reported in this publication was supported by the National Institute on Drug Abuse of the National Institutes for Health under Award Number R01DA042059.