Enhancing Nursing Care Records with A Spoken Dialogue System based on Smartphones
Reviewed, Featured
Tittaya Mairittha, Nattaya Mairittha, Sozo Inoue,
The 40th Joint Conference on Asia-Pacific Association of Medical Informatics (APAMI)
(Not Available)
(Not Available)
8 pages
2020-11-21
Background and Objective: This study describes the integration of a spoken dialogue system and
nursing records on an Android smartphone application intending to help nurses reduce documentation
time and improve the overall experience of a healthcare setting. The application also incorporates with
collecting personal sensor data and activity labels for activity recognition.
Methods: We developed a joint model based on a bidirectional long-short term memory and conditional random fields (Bi-LSTM-CRF) to identify user intention and extract record details from user
utterances. Then, we transformed unstructured data into record inputs on the smartphone application.
Results: The joint model achieved the highest F1-score at 96.79%. Moreover, we conducted an experiment to demonstrate the proposed model’s capability and feasibility in recording in realistic settings.
Our preliminary evaluation results indicate that when using the dialogue-based, we could increase the
percentage of documentation speed to 58.13% compared to the traditional keyboard-based.
Conclusions: Based on our findings, we highlight critical and promising future research directions
regarding the design of the efficient spoken dialogue system and nursing records.
nursing records on an Android smartphone application intending to help nurses reduce documentation
time and improve the overall experience of a healthcare setting. The application also incorporates with
collecting personal sensor data and activity labels for activity recognition.
Methods: We developed a joint model based on a bidirectional long-short term memory and conditional random fields (Bi-LSTM-CRF) to identify user intention and extract record details from user
utterances. Then, we transformed unstructured data into record inputs on the smartphone application.
Results: The joint model achieved the highest F1-score at 96.79%. Moreover, we conducted an experiment to demonstrate the proposed model’s capability and feasibility in recording in realistic settings.
Our preliminary evaluation results indicate that when using the dialogue-based, we could increase the
percentage of documentation speed to 58.13% compared to the traditional keyboard-based.
Conclusions: Based on our findings, we highlight critical and promising future research directions
regarding the design of the efficient spoken dialogue system and nursing records.