Detecting inpatient falls by using natural language processing of electronic medical records

Incident reporting is the most common method for detecting adverse events in a hospital. However, under-reporting or non-reporting and delay in submission of reports are problems that prevent early detection of serious adverse events. The aim of this study was to determine whether it is possible to promptly detect serious injuries after inpatient falls by using a natural language processing method and to determine which data source is the most suitable for this purpose.

We tried to detect adverse events from narrative text data of electronic medical records by using a natural language processing method. We made syntactic category decision rules to detect inpatient falls from text data in electronic medical records. We compared how often the true fall events were recorded in various sources of data including progress notes, discharge summaries, image order entries and incident reports. We applied the rules to these data sources and compared F-measures to detect falls between these data sources with reference to the results of a manual chart review. The lag time between event occurrence and data submission and the degree of injury were compared.

We made 170 syntactic rules to detect inpatient falls by using a natural language processing method. Information on true fall events was most frequently recorded in progress notes (100%), incident reports (65.0%) and image order entries (12.5%). However, F-measure to detect falls using the rules was poor when using progress notes (0.12) and discharge summaries (0.24) compared with that when using incident reports (1.00) and image order entries (0.91). Since the results suggested that incident reports and image order entries were possible data sources for prompt detection of serious falls, we focused on a comparison of falls found by incident reports and image order entries. Injury caused by falls found by image order entries was significantly more severe than falls detected by incident reports (p<0.001), and the lag time between falls and submission of data to the hospital information system was significantly shorter in image order entries than in incident reports (p<0.001).

By using natural language processing of text data from image order entries, we could detect injurious falls within a shorter time than that by using incident reports. Concomitant use of this method might improve the shortcomings of an incident reporting system such as under-reporting or non-reporting and delayed submission of data on incidents.