Background Taiwan has insufficient nursing resources due to the high turnover rate of health care providers. Therefore, reducing the heavy workload of these employees is essential. Herein, speech transcription, which has various potential clinical applications, was employed for the documentation of nursing records. The requirement of including only one speaker per transcription facilitated data collection and system development. Moreover, authorization from patients was unnecessary. Objective The aim of this study was to construct a speech recognition system for nursing records such that health care providers can complete nursing records without typing or with only a few edits. Methods Nursing records in Taiwan are mainly written in Mandarin, with technical terms and abbreviations presented in both Mandarin and English. Therefore, the training set consisted of English code-switching information. Next, transfer learning (TL) and meta-TL (MTL) methods, which perform favorably in code-switching scenarios, were applied. Results As of September 2021, the China Medical University Hospital Artificial Intelligence Speech (CMaiSpeech) data set was established by manually annotating approximately 100 hours of recordings from 525 speakers. The word error rate (WER) of the benchmark model of syllable-based TL was 29.54% in code-switching. The WER of the proposed model of syllable-based MTL was 22.20% in code-switching. The test set comprised 17,247 words. Moreover, in a clinical case, the proposed model of syllable-based MTL yielded a WER of 31.06% in code-switching. The clinical test set contained 1159 words. Conclusions This paper has two main contributions. First, the CMaiSpeech data set—a Mandarin-English corpus—has been established. Health care providers in Taiwan are often compelled to use a mixture of Mandarin and English in nursing records. Second, an automatic speech recognition system for nursing record document conversion was proposed. The proposed system can shorten the work handover time and further reduce the workload of health care providers.

BACKGROUND Taiwan has insufficient nursing resources due to the high turnover rate of nursing personnel. Therefore, reducing the heavy workload of these employees is essential. Herein, speech transcription, which has various potential clinical applications, was employed for the documentation of nursing records. The requirement of only one speaker facilitates data collection and system development. Moreover, authorization from patients is unnecessary. OBJECTIVE A speech recognition system for nursing records was constructed such that medical personnel can complete nursing records without typing or with only a few edits. METHODS Nursing records in Taiwan are mainly written in Mandarin, with technical terms and abbreviations presented in both Mandarin and English. Therefore, the training set consisted of English code-switching (CS) information. Next, transfer learning (TL) and meta-transfer learning (MTL) methods, which perform favorably in CS scenarios, were applied. RESULTS The word error rate (WER) of the benchmark model of syllables-based TL and the proposed model of syllables-based MTL was 29.54% and 22.20% WER in code-switching, respectively. The test set comprised 17,247 words. Moreover, in a clinical case, the proposed model of syllables-based MTL yielded a WER of 31.06% WER in code-switching. The clinical test set contained 1,159 words. CONCLUSIONS Medical personnel in Taiwan are often compelled to use a mixture of Mandarin and English in nursing records. Therefore, a Mandarin–English CS speech recognition system for nursing documentation was developed. The proposed data set has two characteristics, namely the medical field and CS, and lightens the workload of medical personnel.