The digital scribe in clinical practice: a scoping review and research agenda

Abstract The number of clinician burnouts is increasing and has been linked to a high administrative burden. Automatic speech recognition (ASR) and natural language processing (NLP) techniques may address this issue by creating the possibility of automating clinical documentation with a “digital scr...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Marieke M. van Buchem, Hileen Boosman, Martijn P. Bauer, Ilse M. J. Kant, Simone A. Cammel, Ewout W. Steyerberg
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/e521568c3a00458197d5643be70c7aa8
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:e521568c3a00458197d5643be70c7aa8
record_format	dspace
spelling	oai:doaj.org-article:e521568c3a00458197d5643be70c7aa82021-12-02T13:24:15ZThe digital scribe in clinical practice: a scoping review and research agenda10.1038/s41746-021-00432-52398-6352https://doaj.org/article/e521568c3a00458197d5643be70c7aa82021-03-01T00:00:00Zhttps://doi.org/10.1038/s41746-021-00432-5https://doaj.org/toc/2398-6352Abstract The number of clinician burnouts is increasing and has been linked to a high administrative burden. Automatic speech recognition (ASR) and natural language processing (NLP) techniques may address this issue by creating the possibility of automating clinical documentation with a “digital scribe”. We reviewed the current status of the digital scribe in development towards clinical practice and present a scope for future research. We performed a literature search of four scientific databases (Medline, Web of Science, ACL, and Arxiv) and requested several companies that offer digital scribes to provide performance data. We included articles that described the use of models on clinical conversational data, either automatically or manually transcribed, to automate clinical documentation. Of 20 included articles, three described ASR models for clinical conversations. The other 17 articles presented models for entity extraction, classification, or summarization of clinical conversations. Two studies examined the system’s clinical validity and usability, while the other 18 studies only assessed their model’s technical validity on the specific NLP task. One company provided performance data. The most promising models use context-sensitive word embeddings in combination with attention-based neural networks. However, the studies on digital scribes only focus on technical validity, while companies offering digital scribes do not publish information on any of the research phases. Future research should focus on more extensive reporting, iteratively studying technical validity and clinical validity and usability, and investigating the clinical utility of digital scribes.Marieke M. van BuchemHileen BoosmanMartijn P. BauerIlse M. J. KantSimone A. CammelEwout W. SteyerbergNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 4, Iss 1, Pp 1-8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Marieke M. van Buchem Hileen Boosman Martijn P. Bauer Ilse M. J. Kant Simone A. Cammel Ewout W. Steyerberg The digital scribe in clinical practice: a scoping review and research agenda
description	Abstract The number of clinician burnouts is increasing and has been linked to a high administrative burden. Automatic speech recognition (ASR) and natural language processing (NLP) techniques may address this issue by creating the possibility of automating clinical documentation with a “digital scribe”. We reviewed the current status of the digital scribe in development towards clinical practice and present a scope for future research. We performed a literature search of four scientific databases (Medline, Web of Science, ACL, and Arxiv) and requested several companies that offer digital scribes to provide performance data. We included articles that described the use of models on clinical conversational data, either automatically or manually transcribed, to automate clinical documentation. Of 20 included articles, three described ASR models for clinical conversations. The other 17 articles presented models for entity extraction, classification, or summarization of clinical conversations. Two studies examined the system’s clinical validity and usability, while the other 18 studies only assessed their model’s technical validity on the specific NLP task. One company provided performance data. The most promising models use context-sensitive word embeddings in combination with attention-based neural networks. However, the studies on digital scribes only focus on technical validity, while companies offering digital scribes do not publish information on any of the research phases. Future research should focus on more extensive reporting, iteratively studying technical validity and clinical validity and usability, and investigating the clinical utility of digital scribes.
format	article
author	Marieke M. van Buchem Hileen Boosman Martijn P. Bauer Ilse M. J. Kant Simone A. Cammel Ewout W. Steyerberg
author_facet	Marieke M. van Buchem Hileen Boosman Martijn P. Bauer Ilse M. J. Kant Simone A. Cammel Ewout W. Steyerberg
author_sort	Marieke M. van Buchem
title	The digital scribe in clinical practice: a scoping review and research agenda
title_short	The digital scribe in clinical practice: a scoping review and research agenda
title_full	The digital scribe in clinical practice: a scoping review and research agenda
title_fullStr	The digital scribe in clinical practice: a scoping review and research agenda
title_full_unstemmed	The digital scribe in clinical practice: a scoping review and research agenda
title_sort	digital scribe in clinical practice: a scoping review and research agenda
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/e521568c3a00458197d5643be70c7aa8
work_keys_str_mv	AT mariekemvanbuchem thedigitalscribeinclinicalpracticeascopingreviewandresearchagenda AT hileenboosman thedigitalscribeinclinicalpracticeascopingreviewandresearchagenda AT martijnpbauer thedigitalscribeinclinicalpracticeascopingreviewandresearchagenda AT ilsemjkant thedigitalscribeinclinicalpracticeascopingreviewandresearchagenda AT simoneacammel thedigitalscribeinclinicalpracticeascopingreviewandresearchagenda AT ewoutwsteyerberg thedigitalscribeinclinicalpracticeascopingreviewandresearchagenda AT mariekemvanbuchem digitalscribeinclinicalpracticeascopingreviewandresearchagenda AT hileenboosman digitalscribeinclinicalpracticeascopingreviewandresearchagenda AT martijnpbauer digitalscribeinclinicalpracticeascopingreviewandresearchagenda AT ilsemjkant digitalscribeinclinicalpracticeascopingreviewandresearchagenda AT simoneacammel digitalscribeinclinicalpracticeascopingreviewandresearchagenda AT ewoutwsteyerberg digitalscribeinclinicalpracticeascopingreviewandresearchagenda
_version_	1718393125329698816

The digital scribe in clinical practice: a scoping review and research agenda

Ejemplares similares