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

Buchem, Marieke van; Boosman, Hileen; Bauer, Martijn P.; Kant, Ilse; Cammel, Simone A.

doi:10.1038/s41746-021-00432-5

Cited by 50 publications

(34 citation statements)

References 47 publications

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“…The system achieved top-1 accuracy of 0.3464 with an unsupervised approach using cosine similarity of word embeddings [87]. That is, clinicians have only to record an outpatient conversation with some additional voice command, and the NLP system analyzes and summarizes the conversation and converts it into a clinical document in a predefined format [92][93][94][95]. Wang et al developed a digital scribe system, which was 2.17-3.12 times faster than typing and dictation during patient encounter documentation [95].…”

Section: Text Simplificationmentioning

confidence: 99%

Natural Language Processing: from Bedside to Everywhere

et al. 2022

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Objectives: Owing to the rapid progress of natural language processing (NLP), the role of NLP in the medical field has radically gained considerable attention from both NLP and medical informatics. Although numerous medical NLP papers are published annually, there is still a gap between basic NLP research and practical product development. This gap raises questions, such as what has medical NLP achieved in each medical field, and what is the burden for the practical use of NLP? This paper aims to clarify the above questions. Methods: We explore the literature on potential NLP products/services applied to various medical/clinical/healthcare areas. Results: This paper introduces clinical applications (bedside applications), in which we introduce the use of NLP for each clinical department, internal medicine, pre-surgery, post-surgery, oncology, radiology, pathology, psychiatry, rehabilitation, obstetrics, and gynecology. Also, we clarify technical problems to be addressed for encouraging bedside applications based on NLP. Conclusions: These results contribute to discussions regarding potentially feasible NLP applications and highlight research gaps for future studies.

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Section: Text Simplificationmentioning

confidence: 99%

Natural Language Processing: from Bedside to Everywhere

et al. 2022

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“…A microphone is used to record physician-patient conversations and transcribe them to text using automatic speech recognition software. Natural language processing models then extract and summarize relevant information to the physician, which can be used to complete clinical notes, add billing codes, or support a diagnosis with specific extracted information [21].…”

Section: Digital Scribes (Artificial Intelligence)mentioning

confidence: 99%

Interfacing With the Electronic Health Record (EHR): A Comparative Review of Modes of Documentation

Avendano¹,

Gallagher²,

Hawes³

et al. 2022

Cureus

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Electronic health records (EHRs) have provided physicians with a systematic framework for collecting patient data, organizing notes from the healthcare team, and managing the daily workflow in the modern era of healthcare. Despite these advantages, EHRs have proven to be problematic for clinicians. The burdensome regulations requiring increased documentation with the EHR paradigm have led to inefficiencies from dataentry requirements forcing physicians to spend an inordinate amount of time on it, affecting the time available for direct patient care as well as leading to professional burnout. As a result, new modalities such as speech recognition, medical scribes, pre-made EHR templates, and digital scribes [a form of artificial intelligence (AI) based on ambient speech recognition] are increasingly being used to reduce charting time and increase the time available for patient care. The purpose of our review is to provide an up-to-date review of the literature on these modalities including their benefits and shortcomings, to help physicians and other medical professionals choose the best methods to document their patient-care encounters efficiently and effectively.

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“…73 Using automatic speech recognition and natural language processing techniques to automate clinical documentation can be a viable solution to reduce documentation burden but requires more research to demonstrate its technical and clinical validity and utility. 74…”

Section: Focus On Electronic Health Record-based Solutions To Mitigate Clinician Burnoutmentioning

confidence: 99%

A Scoping Review of Health Information Technology in Clinician Burnout

Kim

et al. 2021

Appl Clin Inform

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Background Clinician burnout is a prevalent issue in healthcare, with detrimental implications in healthcare quality and medical costs due to errors. The inefficient use of health information technologies (HIT) is attributed to having a role in burnout. Objective This paper seeks to review the literature with the following two goals: (1) characterize and extract HIT trends in burnout studies over time, and (2) examine the evidence and synthesize themes of HIT's roles in burnout studies. Methods A scoping literature review was performed by following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines with two rounds of searches in PubMed, IEEE Xplore, ACM, and Google Scholar. The retrieved papers and their references were screened for eligibility by using developed inclusion and exclusion criteria. Data were extracted from included papers and summarized either statistically or qualitatively to demonstrate patterns. Results After narrowing down the initial 945 papers, 36 papers were included. All papers were published between 2013 and 2020; nearly half of them focused on primary care (n = 16; 44.4%). The most commonly studied variable was electronic health record (EHR) practices (e.g., number of clicks). The most common study population was physicians. HIT played multiple roles in burnout studies: it can contribute to burnout; it can be used to measure burnout; or it can intervene and mitigate burnout levels. Conclusion This scoping review presents trends in HIT-centered burnout studies and synthesizes three roles for HIT in contributing to, measuring, and mitigating burnout. Four recommendations were generated accordingly for future burnout studies: (1) validate and standardize HIT burnout measures; (2) focus on EHR-based solutions to mitigate clinician burnout; (3) expand burnout studies to other specialties and types of healthcare providers, and (4) utilize mobile and tracking technology to study time efficiency.

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

Cited by 50 publications

References 47 publications

Natural Language Processing: from Bedside to Everywhere

Natural Language Processing: from Bedside to Everywhere

Interfacing With the Electronic Health Record (EHR): A Comparative Review of Modes of Documentation

A Scoping Review of Health Information Technology in Clinician Burnout

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