Studies show positive results for the use of medical scribes, with clinicians spending more face-to-face time with patients and less after-hour time on the EHR 10, 11. Recently, clinicians have hired medical scribes to reduce the administrative burden, i.e., persons who manage administrative tasks, such as summarizing a consultation. Other studies have assessed the relationship between EHR-use and burnout and found that more time spent on the EHR, especially after-hours, was linked to a higher risk of burnout 8, 9. ![]() These administrative tasks decrease clinicians’ career satisfaction 6 and negatively affect the clinician–patient relationship 7. Since the introduction of the electronic health record (EHR), the time spent on administrative tasks has increased to approximately half of a clinician’s workday 3– 5. An important reason is the increasing administrative burden 2. The committee’s extensive report, called Taking Action Against Clinician Burnout, describes reasons for clinician burnout. To investigate this problem, the National Academy of Medicine formed a committee focused on improving patient care by supporting clinician well-being. In a 2017 survey among 5000 US clinicians, 44% reported at least one symptom of burnout 1. In the past few years, clinician burnout has become an acknowledged problem in healthcare. 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. However, the studies on digital scribes only focus on technical validity, while companies offering digital scribes do not publish information on any of the research phases. The most promising models use context-sensitive word embeddings in combination with attention-based neural networks. 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. The other 17 articles presented models for entity extraction, classification, or summarization of clinical conversations. Of 20 included articles, three described ASR models for clinical conversations. ![]() We included articles that described the use of models on clinical conversational data, either automatically or manually transcribed, to automate clinical documentation. 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 reviewed the current status of the digital scribe in development towards clinical practice and present a scope for future research. 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”. The number of clinician burnouts is increasing and has been linked to a high administrative burden.
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