A clinician survey of using speech recognition for clinical documentation in the electronic health record

Goss, Foster R.; Blackley, Suzanne V.; Ortega, Carlos A.; Kowalski, Leigh T.; Landman, Adam B.; Lin, Chen-Tan; Meteer, Marie; Bakes, Samantha; Gradwohl, Stephen C.; Bates, David W.; Zhou, Li

doi:10.1016/j.ijmedinf.2019.07.017

Cited by 44 publications

(40 citation statements)

References 24 publications

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“…Users may encounter some uncertainty when switching from keyboard typing to speech input, and changing their habitual interfaces may be risky for users [4]. Although the speed of speech input is faster than keyboard typing [8], subsequent editing may take more time, such that the efficiency of document processing is reduced [93]. Whether speech input creates risks or barriers for users depend on the users, context, and device [94].…”

Section: Discussion Of Resultsmentioning

confidence: 99%

Why College Students Prefer Typing Over Speech Input: The Dual Perspective

Tsai¹

2021

IEEE Access

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On the basis of innovation resistance theory (IRT) and technology acceptance model (TAM), this study explores the preference of college students for keyboard typing over speech input for document processing. Results showed that functional barriers (i.e., usage, value, and risk barriers) and psychological barriers (i.e., tradition and image barriers) positively affect users' resistance to change. Perceived ease of use and perceived usefulness influence the intention to adopt speech input, which is consistent with TAM. Resistance to change was proven to negatively affect users' intention to adopt speech input. Academically, results confirm that although barriers to speech input currently exist, users still consider speech input as easy and useful and plan to adopt the technology. In practice, speech recognition system companies can significantly enhance users' adoption intentions by reducing barriers and increasing their perception of ease of use and usefulness of speech input.

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Section: Discussion Of Resultsmentioning

confidence: 99%

Why College Students Prefer Typing Over Speech Input: The Dual Perspective

Tsai¹

2021

IEEE Access

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“…Unfortunately, the quality and quantity of routine data available for research is likely to have been affected by the swift de-escalation in documentation and recording of observations that was widely adopted to reduce nurses' workload, particularly in intensive care. Research-based solutions to optimizing the safety and efficiency of nursing documentation are urgently needed and could be aided by artificial intelligence tools such as speech recognition (Goss et al 2019).…”

Section: Discussionmentioning

confidence: 99%

What place does nurse‐led research have in the COVID‐19 pandemic?

Castro-Sánchez

Russell

Dolman

et al. 2021

International Nursing Review

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Aim Reflect upon the visibility of nursing‐led research during the COVID‐19 pandemic. Background The emerging SARS‐CoV‐2 infection has galvanized collaborative and multidisciplinary efforts in clinical and research practice worldwide. The scarce evidence‐base to manage patients with COVID‐19 has included limited nurse‐led research. Introduction Clinical research nurses have greatly contributed to the delivery of COVID‐19 research, yet the number of COVID‐19 nursing‐led research papers appears to be limited, with even fewer nurse‐led research projects funded. Methods Authors’ views and PubMed search on ‘COVID‐19 and nursing’. Findings There is a dearth of nursing‐led research. Most papers describe the nursing contribution to COVID‐19 care, changes in nursing working arrangements and emotional burden. There are opportunities to explore the consequences to vulnerable population groups of public health measures implemented to stop the progress of the COVID‐19 pandemic. Discussion Workforce gaps, limited integration in research structures and clinical redeployment may have hampered nurse‐led research. COVID‐19 may exacerbate staffing deficits by disrupting the education pipeline, obstructing the transition from clinical to academic practice, particularly in areas where clinical academic roles are yet to emerge. Conclusion The absence of nurse‐led research in COVID‐19 can be explained by chronic, underlying factors and the features of the pandemic response. Emerging models of care, effective staffing and inequalities related to COVID‐19 appear obvious research areas. Nursing leadership needs to strengthen its political voice and lobbying skills to secure nurse‐led research funding. Implications for Nursing Policy Embracing international nursing research, strengthening collaborations and lobbying policymakers for investment in nurse‐sensitive research would enhance the response to COVID‐19.

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“…Despite unresolved challenges, application in radiology departments has already shown benefits in reducing reporting time and costs, as well as increasing productivity. 61 Furthermore, AI could serve as a "fail-proof" for study reporting ─echocardiography reports can be analysed by artificial neural networks to predict patient mortality and hospital readmissions for heart failure patients. 62 NLP can help clinical interpretation of reports and report drafting by assessing posttest risk after myocardial perfusion imaging, 63 where underestimation of ischemia in reporting has been previously noted.…”

Section: Study Reportingmentioning

confidence: 99%

Integration of artificial intelligence into clinical patient management: focus on cardiac imaging

Lončarić

Cámara

Piella

et al. 2021

Revista Española de Cardiología (English Edition)

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Cardiac imaging is a crucial component in the management of cardiac patients, and as such it influences multiple, inter-related parts of the clinical workflow: physician-patient contact, image acquisition, image pre-and post-processing, study reporting, diagnostics and outcome predictions, medical interventions, and, finally, knowledge-building through clinical research. With the gradual and ubiquitous infiltration of artificial intelligence into cardiology, it has become clear that, when used appropriately, it will influence and potentially improve -through automation, standardization and data integration─ all components of the clinical workflow. The aim of this review is to present a comprehensive vision of a full integration of artificial intelligence into the standard clinical patient management ─with a focus on cardiac imaging, but applicable to all information handling-and discuss current barriers that remain to be overcome before the wide-spread implementation and integration.

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A clinician survey of using speech recognition for clinical documentation in the electronic health record

Cited by 44 publications

References 24 publications

Why College Students Prefer Typing Over Speech Input: The Dual Perspective

Why College Students Prefer Typing Over Speech Input: The Dual Perspective

What place does nurse‐led research have in the COVID‐19 pandemic?

Integration of artificial intelligence into clinical patient management: focus on cardiac imaging

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