Talk to the Veterans Crisis Line now
U.S. flag
An official website of the United States government

VA Health Systems Research

Go to the VA ORD website
Go to the QUERI website

HSR Citation Abstract

Search | Search by Center | Search by Source | Keywords in Title

Electronic approaches to making sense of the text in the adverse event reporting system.

Benin AL, Fodeh SJ, Lee K, Koss M, Miller P, Brandt C. Electronic approaches to making sense of the text in the adverse event reporting system. Journal of healthcare risk management : the journal of the American Society for Healthcare Risk Management. 2016 Aug 1; 36(2):10-20.

Dimensions for VA is a web-based tool available to VA staff that enables detailed searches of published research and research projects.

If you have VA-Intranet access, click here for more information

VA staff not currently on the VA network can access Dimensions by registering for an account using their VA email address.
   Search Dimensions for VA for this citation
* Don't have VA-internal network access or a VA email address? Try searching the free-to-the-public version of Dimensions


INTRODUCTION: Health care organizations working to eliminate preventable harm and to improve patient safety must have robust programs to collect and to analyze data on adverse events in order to use the information to affect improvement. Such adverse event reporting systems are based on frontline personnel reporting issues that arise in the course of their daily work. Limitations in how existing software systems handle these reports mean that use of this potentially rich information is resource intensive and prone to variable results. AIM: The aim of this study was to develop an electronic approach to processing the text in medical event reports that would be reliable enough to be used to improve patient safety. METHODS: At Connecticut Children's Medical Center, staff manually enter reports of adverse events into a web-based software tool. We evaluated the ability of 2 electronic methods-rule-based query and semi-supervised machine learning-to identify specific types of events ("use cases") versus a reference standard. Rule-based query was tested on 5 use cases and machine learning on a subset of 2 using 9164 events reported from February 2012-January 2014. RESULTS: Machine learning found 93% of the weight-based errors and 92% of the errors in patient-identification. Rule-based query had accuracy of 99% or greater, high precision, and high recall for all use cases. CONCLUSIONS: Electronic approaches to streamlining the use of adverse event reports are feasible to automate and valuable for categorizing this important data for use in improving patient safety.

Questions about the HSR website? Email the Web Team

Any health information on this website is strictly for informational purposes and is not intended as medical advice. It should not be used to diagnose or treat any condition.