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.
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.