Ambient clinical documentation technology uses automatic speech recognition (ASR) and natural language processing (NLP) to turn patient–clinician conversations into clinical documentation. It is a promising approach to reducing clinician burden and improving documentation quality. However, the performance of current-generation ASR remains inadequately validated. In this study, we investigated the impact of non-lexical conversational sounds (NLCS) on ASR performance. NLCS, such as Mm-hm and Uh-uh, are commonly used to convey important information in clinical conversations, for example, Mm-hm as a “yes” response from the patient to the clinician question “are you allergic to antibiotics?” In this study, we evaluated 2 contemporary ASR engines, Google Speech-to-Text Clinical Conversation (“Google ASR”), and Amazon Transcribe Medical (“Amazon ASR”), both of which have their language models specifically tailored to clinical conversations. The empirical data used were from 36 primary care encounters. We conducted a series of quantitative and qualitative analyses to examine the word error rate (WER) and the potential impact of misrecognized NLCS on the quality of clinical documentation. Out of a total of 135 647 spoken words contained in the evaluation data, 3284 (2.4%) were NLCS. Among these NLCS, 76 (0.06% of total words, 2.3% of all NLCS) were used to convey clinically relevant information. The overall WER, of all spoken words, was 11.8% for Google ASR and 12.8% for Amazon ASR. However, both ASR engines demonstrated poor performance in recognizing NLCS: the WERs across frequently used NLCS were 40.8% (Google) and 57.2% (Amazon), respectively; and among the NLCS that conveyed clinically relevant information, 94.7% and 98.7%, respectively. Current ASR solutions are not capable of properly recognizing NLCS, particularly those that convey clinically relevant information. Although the volume of NLCS in our evaluation data was very small (2.4% of the total corpus; and for NLCS that conveyed clinically relevant information: 0.06%), incorrect recognition of them could result in inaccuracies in clinical documentation and introduce new patient safety risks.

Objective: To investigate the effect of smoking on choroidal microvasculature dropout (MvD) in glaucoma.Design: Cross-sectional study.Setting: Tertiary glaucoma centre.Participants: 223 eyes of 163 patients with primary open-angle glaucoma who had undergone imaging with optical coherence tomography angiography and completed a questionnaire on smoking from the Diagnostic Innovations in Glaucoma Study.Primary outcome measures: Linear mixed-effects models were used to determine the effect of each parameter on MvD area and angular circumference. The sensitivity analysis was performed by categorising the glaucoma severity determined by visual field mean deviation (MD).Results: MvD was found in 37 (51.4%) eyes with smoking history and in 67 (44.4%) eyes with non-smokers (p=0.389). Larger MvD area and wider angular circumference were found in smokers compared with non-smokers (p=0.068 and p=0.046, respectively). In a multivariable model, smoking intensity was significantly associated with MvD area (0.30(95% CI 0.01 to 0.60) each 0.01 mm²per 10 pack-years; p=0.044). In eyes with moderate-severe glaucoma (MD <−6), smoking intensity was associated with larger MvD area (0.47 (95% CI 0.11 to 0.83) each 0.01 mm²per 10 pack-years; p=0.011), whereas no significant association was found in early glaucoma (MD ≥−6) (−0.08 (95% CI −0.26 to 0.11), p=0.401).Conclusions: Smoking intensity was associated with larger choroidal MvD area in eyes with glaucoma, especially in patients with more severe disease.Trial registration number: NCT00221897.