18, 19 The position of the recording device also has a strong impact on the captured audio. 17 These are best-case scenario results for current ASR technologies, as the recordings were made in a controlled environment, under near-ideal acoustic conditions, with speakers simulating a medical conversation while sitting in front of a microphone.Ī recording made in a real clinical setting is likely to include noise and other environmental conditions that negatively affect ASR. A recent study found that the word error rate of simulated medical conversations with commercial ASR engines was 35% or higher. High-quality audio minimizes errors across the processing pipeline of the digital scribe. The first step for a digital scribe is recording the audio of a clinician–patient conversation.
7 Recently, advances in AI, machine learning (ML), NLP, natural language understanding, and automatic speech recognition (ASR), have raised the prospect of deploying effective and reliable digital scribes in clinical practice. The implementation of a digital scribe consists of a pipeline of speech-processing and natural language processing (NLP) modules. To generate medical notes for the clinician–patient encounter, a digital scribe must be able to: (1) record the clinician–patient conversation, (2) convert the audio to text, and (3) extract salient information from the text and summarize the information (Fig. Digital scribes can also be referred to as autoscribes, automated scribes, virtual medical scribes, artificial intelligence (AI) powered medical notes, speech recognition-assisted documentation, and smart medical assistants. Along with academic research into digital scribes, a growing number of companies are also playing in the digital scribe space, including Microsoft, Google, EMR.AI, Suki, Robin Healthcare, DeepScribe, Tenor.ai, Saykara, Sopris Health, Carevoice, Notable, and Kiroku. Interest in digital scribes has increased rapidly. Reducing the time and effort invested by clinicians in the documentation process also has the potential to increase productivity, decrease clinician burnout, and improve the clinician–patient relationship, leading to higher quality and patient-centered care.
5, 6, 7, 8, 9 In theory, a digital scribe would enable a clinician to fully engage with a patient, maintain eye contact, and eliminate the need to split attention by turning to a computer to manually document the encounter. A digital scribe is an automated clinical documentation system able to capture the clinician–patient conversation and then generate the documentation for the encounter, like the function performed by human medical scribes. 4 Ideally, clinical documentation would be an automated process, with only the minimally necessary input from humans.
Clinical documentation is found to be associated with clinician burnout, 1 increased cognitive load, 2 information loss, 3 and distractions.
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