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.

Accuracy of Cloud-Based Speech Recognition Open Application Programming Interface for Medical Terms of Korean

“… 1 13 14 Despite this, evidence about the benefits of SR usage for clinical documentation is implausible and relatively neutral due to its own barriers, such as the requirement of training to use SR and interoperability with existing systems. 1 15 Most current medical SR systems target physicians’ medical documentation, such as radiologic or pathologic reports, and a system for narrative documentation between the doctor and patient is lacking. 1 In addition, the implementation costs of an SR system may offset the clinical benefits.…”

A Qualitative Thematic Analysis of Addressing the Why: An Artificial Intelligence (AI) in Healthcare Symposium

“…The discussion and exploration of knowledge augmentation algorithms will continue, and the potential for bias should not be taken lightly. Companies utilize AI in conjunction with EMR systems to improve a provider’s documentation time, make suggestions, and create notes using ambient voice technology, all within a workflow augmentation paradigm [ 7 , 13 ]. In one article, ambient voice technology with natural language processing may forever alter the way a provider documents, allowing for more time with the patient and less time on chart documentation [ 13 ].…”

“…Companies utilize AI in conjunction with EMR systems to improve a provider’s documentation time, make suggestions, and create notes using ambient voice technology, all within a workflow augmentation paradigm [ 7 , 13 ]. In one article, ambient voice technology with natural language processing may forever alter the way a provider documents, allowing for more time with the patient and less time on chart documentation [ 13 ]. Barriers to implementation include upfront cost, training, data sharing, and compatibility with the existing technology [ 13 , 14 ].…”

“…In one article, ambient voice technology with natural language processing may forever alter the way a provider documents, allowing for more time with the patient and less time on chart documentation [ 13 ]. Barriers to implementation include upfront cost, training, data sharing, and compatibility with the existing technology [ 13 , 14 ]. The authors also discussed bias and risk to data privacy as potential issues to be aware of [ 13 ].…”

“…Barriers to implementation include upfront cost, training, data sharing, and compatibility with the existing technology [ 13 , 14 ]. The authors also discussed bias and risk to data privacy as potential issues to be aware of [ 13 ]. True knowledge augmentation algorithms should be developed without bias and meticulously follow evidence-based diagrams.…”