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Digital scribe utility and barriers to implementation in clinical practice: a scoping review.

ABSTRACT: Electronic health records (EHRs) allow for meaningful usage of healthcare data. Their adoption provides clinicians with a central location to access and share data, write notes, order labs and prescriptions, and bill for patient visits. However, as non-clinical requirements have increased, time spent using EHRs eclipsed time spent on direct patient care. Several solutions have been proposed to minimize the time spent using EHRs, though each have limitations. Digital scribe technology uses voice-to-text software to convert ambient listening to meaningful medical notes and may eliminate the physical task of documentation, allowing physicians to spend less time on EHR engagement and more time with patients. However, adoption of digital scribe technology poses many barriers for physicians. In this study, we perform a scoping review of the literature to identify barriers to digital scribe implementation and provide solutions to address these barriers. We performed a literature review of digital scribe technology and voice-to-text conversion and information extraction as a scope for future research. Fifteen articles met inclusion criteria. Of the articles included, four were comparative studies, three were reviews, three were original investigations, two were perspective pieces, one was a cost-effectiveness study, one was a keynote address, and one was an observational study. The published articles on digital scribe technology and voice-to-text conversion highlight digital scribe technology as a solution to the inefficient interaction with EHRs. Benefits of digital scribe technologies included enhancing clinician ability to navigate charts, write notes, use decision support tools, and improve the quality of time spent with patients. Digital scribe technologies can improve clinic efficiency and increase patient access to care while simultaneously reducing physician burnout. Implementation barriers include upfront costs, integration with existing technology, and time-intensive training. Technological barriers include adaptability to linguistic differences, compatibility across different clinical encounters, and integration of medical jargon into the note. Broader risks include automation bias and risks to data privacy. Overcoming significant barriers to implementation will facilitate more widespread adoption.

Supplementary information

The online version contains supplementary material available at 10.1007/s12553-021-00568-0.

SUBMITTER: Ghatnekar S

PROVIDER: S-EPMC8169416 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ObjectiveAlthough artificial intelligence (AI) has demonstrated promise in enhancing breast cancer diagnosis, the implementation of AI algorithms in clinical practice encounters various barriers. This scoping review aims to identify these barriers and facilitators to highlight key considerations for developing and implementing AI solutions in breast cancer imaging.MethodA literature search was conducted from 2012 to 2022 in six databases (PubMed, Web of Science, CINHAL, Embase, IEEE, and ArXiv). The articles were included if some barriers and/or facilitators in the conception or implementation of AI in breast clinical imaging were described. We excluded research only focusing on performance, or with data not acquired in a clinical radiology setup and not involving real patients.ResultsA total of 107 articles were included. We identified six major barriers related to data (B1), black box and trust (B2), algorithms and conception (B3), evaluation and validation (B4), legal, ethical, and economic issues (B5), and education (B6), and five major facilitators covering data (F1), clinical impact (F2), algorithms and conception (F3), evaluation and validation (F4), and education (F5).ConclusionThis scoping review highlighted the need to carefully design, deploy, and evaluate AI solutions in clinical practice, involving all stakeholders to yield improvement in healthcare.Clinical relevance statementThe identification of barriers and facilitators with suggested solutions can guide and inform future research, and stakeholders to improve the design and implementation of AI for breast cancer detection in clinical practice.Key points• Six major identified barriers were related to data; black-box and trust; algorithms and conception; evaluation and validation; legal, ethical, and economic issues; and education. • Five major identified facilitators were related to data, clinical impact, algorithms and conception, evaluation and validation, and education. • Coordinated implication of all stakeholders is required to improve breast cancer diagnosis with AI.

Project description:Background:Inactivity and sedentary lifestyle have led experts to recommend an increase in structured, workplace-based physical activity (PA) initiatives. Previous studies on workplace-based PA have only shown moderate and short-term effects. This has been attributed to the lack of clear implementation strategies and understanding of factors that may hinder or enable uptake of PA. To ensure long-term, sustainable outcomes, there is a need for a better understanding of implementation strategies, and barriers and facilitators to workplace-based PA. Method:A scoping review of studies investigating implementation approaches and factors affecting uptake of workplace-based PA was conducted. Qualitative and quantitative articles published in MEDLINE, Embase, Scopus, or PsycINFO between 2008 and 2018 evaluating the implementation of PA were included. Data on study characteristics, evaluation, and implementation methods applied were systematically extracted. Two reviewers extracted, coded, and organised factors affecting uptake using the Theoretical Domains Framework (TDF). Results:After dual, blinded screening of titles and abstracts, 16 articles reporting on eight studies were included in the review. Several different methods of implementation were applied, including information meeting, kick-off events, and "change agents" as the most common. A total of 109 factors influencing implementation were identified, consisting of 57 barriers and 52 facilitators. Barriers most often related to the TDF domains Environmental Context and Resources (n?=?34, 36.2%), Social influences (n?=?13, 13.8%), and Social/Professional Role and Identity (n?=?8, 8.5%). Likewise, facilitators most often related to the TDF domains Social influences (n?=?17, 19.5%), Environmental Context and Resources (n?=?16, 18.4%), and Social/Professional Role and Identity (n?=?9, 10.3%). Conclusion:Our review has highlighted the multilevel factors affecting the uptake of workplace-based PA and underpins the complexities in implementation of such initiatives. The published literature predominantly provides details from the employees' perspectives on factors that need to be addressed and a lack of attention to these factors will cause them to hamper uptake of PA. The analysis of barriers and facilitators provides a theoretical foundation to guide future intervention design. However, further research is needed to fully understand the success or failure of implementation processes.

Project description:BackgroundThe digitization of health care led to a steady increase in the adoption and use of mobile health (mHealth) apps. Germany is the first country in the world to cover the costs of mHealth apps through statutory health insurance. Although the benefits of mHealth apps are discussed in detail, aspects of problems and barriers are rarely studied.ObjectiveThis scoping review aimed to map and categorize the evidence on problems and barriers related to the use of mHealth apps.MethodsSystematic searches were conducted in the MEDLINE, Embase, and PsycINFO databases. Additional searches were conducted on JMIR Publications and on websites of relevant international organizations. The inclusion criteria were publications dealing with apps similar to those approved in the German health care system, publications addressing problems and barriers related to the use of mHealth apps, and articles published between January 1, 2015, and June 8, 2021. Study selection was performed by 2 reviewers. The manuscript was drafted according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist. The analysis of the included publications and categorization of problems and hurdles were performed using MAXQDA (VERBI Software GmbH).ResultsThe database search identified 1479 publications. Of the 1479 publications, 21 (1.42%) met the inclusion criteria. A further 8 publications were included from citation searching and searching in JMIR Publications. The identified publications were analyzed for problems and barriers. Problems and barriers were classified into 10 categories ("validity," "usability," "technology," "use and adherence," "data privacy and security," "patient-physician relationship," "knowledge and skills," "individuality," "implementation," and "costs"). The most frequently mentioned categories were use and adherence (eg, incorporating the app into daily life or dropouts from use; n=22) and usability (eg, ease of use and design; n=19).ConclusionsThe search identified various problems and barriers in the context of mHealth apps. Although problems at the app level (such as usability) are studied frequently, problems at the system level are addressed rather vaguely. To ensure optimal use of and care with mHealth apps, it is essential to consider all types of problems and barriers. Therefore, researchers and policy makers should have a special focus on this issue to identify the needs for quality assurance.International registered report identifier (irrid)RR2-10.2196/32702.

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Digital scribe utility and barriers to implementation in clinical practice: a scoping review.

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