A usability framework for speech recognition technologies in clinical handover: A pre-implementation study

Dawson, Linda; Johnson, Maree; Suominen, Hanna; Basilakis, Jim; Sanchez, Paula; Estival, Dominique; Kelly, Barbara F.; Hanlen, Leif

doi:10.1007/s10916-014-0056-7

Cited by 18 publications

(21 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The technical pipeline (ie, recorded voice, transcription, analysis) has been validated in clinical settings and published [ 36 , 60 , 61 ]. We have also evaluated the model of the handover [ 60 , 61 ] and systematically reviewed relevant technical literature [ 62 ].…”

Section: Resultsmentioning

confidence: 99%

“…Although the data we provided are a simulation of nursing handover, the written text for the handover scenario was based upon 150 live audio recordings of nursing handover in several Sydney-based hospitals [ 36 , 60 , 61 ]. These recordings were manually transcribed under confidentiality conditions and the results used to inspire new handover scenarios.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Benchmarking Clinical Speech Recognition and Information Extraction: New Data, Methods, and Evaluations

Suominen¹,

Zhou²,

Hanlen³

et al. 2015

JMIR Med Inform

Self Cite

View full text Add to dashboard Cite

BackgroundOver a tenth of preventable adverse events in health care are caused by failures in information flow. These failures are tangible in clinical handover; regardless of good verbal handover, from two-thirds to all of this information is lost after 3-5 shifts if notes are taken by hand, or not at all. Speech recognition and information extraction provide a way to fill out a handover form for clinical proofing and sign-off.ObjectiveThe objective of the study was to provide a recorded spoken handover, annotated verbatim transcriptions, and evaluations to support research in spoken and written natural language processing for filling out a clinical handover form. This dataset is based on synthetic patient profiles, thereby avoiding ethical and legal restrictions, while maintaining efficacy for research in speech-to-text conversion and information extraction, based on realistic clinical scenarios. We also introduce a Web app to demonstrate the system design and workflow.MethodsWe experiment with Dragon Medical 11.0 for speech recognition and CRF++ for information extraction. To compute features for information extraction, we also apply CoreNLP, MetaMap, and Ontoserver. Our evaluation uses cross-validation techniques to measure processing correctness.ResultsThe data provided were a simulation of nursing handover, as recorded using a mobile device, built from simulated patient records and handover scripts, spoken by an Australian registered nurse. Speech recognition recognized 5276 of 7277 words in our 100 test documents correctly. We considered 50 mutually exclusive categories in information extraction and achieved the F1 (ie, the harmonic mean of Precision and Recall) of 0.86 in the category for irrelevant text and the macro-averaged F1 of 0.70 over the remaining 35 nonempty categories of the form in our 101 test documents.ConclusionsThe significance of this study hinges on opening our data, together with the related performance benchmarks and some processing software, to the research and development community for studying clinical documentation and language-processing. The data are used in the CLEFeHealth 2015 evaluation laboratory for a shared task on speech recognition.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Benchmarking Clinical Speech Recognition and Information Extraction: New Data, Methods, and Evaluations

Suominen¹,

Zhou²,

Hanlen³

et al. 2015

JMIR Med Inform

Self Cite

View full text Add to dashboard Cite

show abstract

“…The most apparent use for voice assistants is transforming electronic health record (EHR) interactions. This field has received significant attention in recent years, not only among academic communities but also in clinical settings [ 1 , 2 , 3 ].…”

Section: Introductionmentioning

confidence: 99%

Automatic Labeled Dialogue Generation for Nursing Record Systems

Mairittha

Inoue

2020

JPM

View full text Add to dashboard Cite

The integration of digital voice assistants in nursing residences is becoming increasingly important to facilitate nursing productivity with documentation. A key idea behind this system is training natural language understanding (NLU) modules that enable the machine to classify the purpose of the user utterance (intent) and extract pieces of valuable information present in the utterance (entity). One of the main obstacles when creating robust NLU is the lack of sufficient labeled data, which generally relies on human labeling. This process is cost-intensive and time-consuming, particularly in the high-level nursing care domain, which requires abstract knowledge. In this paper, we propose an automatic dialogue labeling framework of NLU tasks, specifically for nursing record systems. First, we apply data augmentation techniques to create a collection of variant sample utterances. The individual evaluation result strongly shows a stratification rate, with regard to both fluency and accuracy in utterances. We also investigate the possibility of applying deep generative models for our augmented dataset. The preliminary character-based model based on long short-term memory (LSTM) obtains an accuracy of 90% and generates various reasonable texts with BLEU scores of 0.76. Secondly, we introduce an idea for intent and entity labeling by using feature embeddings and semantic similarity-based clustering. We also empirically evaluate different embedding methods for learning good representations that are most suitable to use with our data and clustering tasks. Experimental results show that fastText embeddings produce strong performances both for intent labeling and on entity labeling, which achieves an accuracy level of 0.79 and 0.78 f1-scores and 0.67 and 0.61 silhouette scores, respectively.

show abstract

“…A different approach that has been studied and integrated into EHR technology. For example, entering data into electronic forms (e-forms) on a smartphone application [5,6] or using speech recognition technology, which is a technology that allows spoken input into systems [7,8,9]. Speech recognition enables hands-free control of various devices and helps to capture speech at a faster rate than type speed.…”

Section: Introductionmentioning

confidence: 99%

“…The speech recognition technology can translate speech to text, which is commonly faster than typing, and it has a capability that allows nurses to record while they are taking care of patients (hands-free mode) or when they have jobs that occupy their hands, they would greatly benefit from a voice-controlled environment [7,8,9]. Although EHR using speech recognition success has been reported, full adoption of the system into the real care nursing practice is still in the distant future.…”

Section: Introductionmentioning

confidence: 99%

Evaluating a Spoken Dialogue System for Recording Systems of Nursing Care

Mairittha

Inoue

2019

Sensors

View full text Add to dashboard Cite

Integrating speech recondition technology into an electronic health record (EHR) has been studied in recent years. However, the full adoption of the system still faces challenges such as handling speech errors, transforming raw data into an understandable format and controlling the transition from one field to the next field with speech commands. To reduce errors, cost, and documentation time, we propose a dialogue system care record (DSCR) based on a smartphone for nursing documentation. We describe the effects of DSCR on (1) documentation speed, (2) document accuracy and (3) user satisfaction. We tested the application with 12 participants to examine the usability and feasibility of DSCR. The evaluation shows that DSCR can collect data efficiently by achieving 96% of documentation accuracy. Average documentation speed was increased by 15% (P = 0.012) compared to traditional electronic forms (e-forms). The participants’ average satisfaction rating was 4.8 using DSCR compared to 3.6 using e-forms on a scale of 1–5 (P = 0.032).

show abstract

A usability framework for speech recognition technologies in clinical handover: A pre-implementation study

Cited by 18 publications

References 34 publications

Benchmarking Clinical Speech Recognition and Information Extraction: New Data, Methods, and Evaluations

Benchmarking Clinical Speech Recognition and Information Extraction: New Data, Methods, and Evaluations

Automatic Labeled Dialogue Generation for Nursing Record Systems

Evaluating a Spoken Dialogue System for Recording Systems of Nursing Care

Contact Info

Product

Resources

About