Paper Info
Title
Integrating a spoken dialogue system, nursing records, and activity data collection based on smartphones
Abstract
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. (c) 2021 Elsevier B.V. All rights reserved.
Year
DOI
Venue
2021
10.1016/j.cmpb.2021.106364
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
Keywords
DocType
Volume
Electronic health record, Nursing record, Dialogue system, Intent classification, Entity extraction, Activity recognition
Journal
210
ISSN
Citations 
PageRank 
0169-2607
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Tittaya Mairittha114.74
Nattaya Mairittha224.77
Sozo Inoue317658.17