Abstract
Background: The development of speech-recognition systems has progressed considerably, and introducing the system is expected to improve the efficiency of nursing documentation. The clinical research focus has shifted to the usability of documentation systems and knowledge-based discrimination of spoken words. However, differences between spoken and written words could create a bottleneck effect when using speech recognition. To consider the impact of the bottleneck, focusing on circulating nurses, we investigated the extent to which their spoken words corresponded to their nursing records.
Methods: For our investigation we opted for a gynecological laparoscopic surgery which is usually an elective operation. We obtained spoken and nursing record data (hereafter referred to as voice data and report data, respectively) from three surgeries. We first conducted a morphological analysis, and then analyzed the noun morphemes. The noun morphemes from the report data were classified into categories and subcategories, and those from the voice data were compared with the corresponding categories.
Results & Discussion: We obtained 9,220 and 552 morphemes from the voice and report data, of which 2,370 and 450 were noun morphemes, respectively. Within the noun morphemes from the report data, 26.2% were exactly the same as those in the voice data. 63.2% of the noun words in the report data were rephrased in different terms in the voice data. 10.5% of the noun words appeared only in the report data and not in the voice data. We found a high correspondence between voice data and report data. This study demonstrates the possibility of extracting keywords to create a surgical nursing record from the spoken words of circulating nurses. However, many instances of rephrasing were observed. These data suggest the necessity of linking terms used in utterances and documentation.
