Detection of adverse events in electronic medical records using natural language processing

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Incident reporting system is widely used in hospital to detect patient safety incidents and adverse events. However, there are several problems in the system such as under-reporting and lag-time between events and submission of the reports. To resolve these issues, we tried to detect incidents by using natural language processing of electronic medical records. We constructed syntactic-semantic category decision rules and applied these rules to narrative text data in medical records. As for inpatient falls, these rules showed satisfactory performance(sensitivity 86. 5% and specificity 97. 5%). Progress notes and discharge summaries were not appropriate for data sources to be used in this method because of their low specificity or low sensitivity. In contrast, natural language processing of text data on image order entries enables rapid detection of injurious falls. This method is useful to compensate the shortcomings of incident reporting system such as the under reporting and the lag-time.