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Digital scribe8/3/2023 ![]() ![]() There is little consensus on the core features that should constitute a scribe, and little exploration of how best to craft the human–computer interaction between clinician, computer and patient. However, while AI may allow us to reconceive the clinical documentation task, the risks and benefits of this rapidly emerging new class of clinical system remains largely unstudied. ![]() 12– 14 We thus appear to be on the cusp of a major change in the way electronic documentation is undertaken. 11 More likely, human scribes are a role model for a new generation of documentation technology – the digital scribe.ĭigital scribes employ advances in speech recognition (SR), natural language processing, and AI to provide clinicians with tools to automatically document elements of the spoken clinical encounter. 7– 9 Some argue scribes are an impediment to the evolution of EHR technology, because they reduce the pressure on innovation, 10 but this EHR ‘workaround’ also liberates clinicians to focus more on patients. Having scribes on the team can improve revenue and patient/provider satisfaction, and may also improve patient throughput. Scribes are trained to work with clinicians, translating information in clinical encounters into meaningful and accurate records, and allow clinicians to better focus on the clinical aspects of the consultation. 6Ĭlinical scribes, more common in North America, were introduced to reduce the burden of electronic documentation on clinicians. 5 There is thus a strong case that the EHR, whilst necessary for effective care, is in dire need of reinvention. Our conversation with the patient has been replaced by computation of the record. Record keeping, rather than being a by-product of the patient encounter, has become its primary orchestrator. EHR design is predicated on the primacy of the documentation task, and rarely pays heed to the other tasks within the clinical encounter. This is because modern EHRs are, in form and function, a digital translation of paper-based records. 1– 3 Current generation EHRs, which rely on clinicians to either type or dictate notes, typically are also not on their own sufficient to improve patient outcomes, but do improve the quality of clinical documentation. The EHR has been associated with decreased clinician satisfaction, increased documentation times, reduced quality and length of interaction with patients, new classes of patient safety risk, and substantial investment costs for providers. Nothing appears to cause more frustration for many clinicians than the electronic health record (EHR). Digital scribes promisingly offer a gateway into the clinical workflow for more advanced support for diagnostic, prognostic and therapeutic tasks. The electronic record also shifts from a human created summary of events to potentially a full audio, video and sensor record of the clinical encounter. Automation bias may see clinicians automatically accept scribe documents without checking. ![]() Digital scribes raise many issues for clinical practice, including new patient safety risks. Data from clinical instruments can be automatically transmitted, interpreted using AI and entered directly into the record. Intelligent clinical environments permit such augmented clinical encounters to occur in a fully digitised space where the environment becomes the computer. Computer-led systems are delegated full control of documentation and only request human interaction when exceptions are encountered. Mixed-initiative systems are delegated part of the documentation task, converting the conversations in a clinical encounter into summaries suitable for the electronic record. Human led systems task clinicians with creating documentation, but provide tools to make the task simpler and more effective, for example with dictation support, semantic checking and templates. Whilst in their infancy, digital scribes are likely to evolve through three broad stages. Digital scribes or intelligent documentation support systems, take advantage of advances in speech recognition, natural language processing and artificial intelligence, to automate the clinical documentation task currently conducted by humans. Current generation electronic health records suffer a number of problems that make them inefficient and associated with poor clinical satisfaction. ![]()
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