This rapid review looked at whether AI-enabled digital scribe technologies improve clinical documentation efficiency, physician well-being, and patient experience in real-world health care settings, using the QUEST Human Evaluation Framework, the Systems Engineering Initiative for Patient Safety (SEIPS) 3.0 model, and Abbasian et al.’s metrics to assess digital scribe implementation. Researchers analyzed n = 6 studies drawn from academic and community health systems between 2014 and 2024, focusing on tools such as Nuance DAX and similar ambient listening systems. Studies were identified through MEDLINE, Embase, Web of Science, Cochrane, and PubMed Central using broad AI-related search criteria. The review applied the QUEST human evaluation framework and SEIPS 3.0 model to assess information quality, workflow effects, and safety. Models were compared against manual documentation and standard clinician practice. Across studies, digital scribes reduced documentation time by 22–29% per encounter (e.g., 5.3 to 4.8 minutes per note) and had modest declines in after-hours “pajama time.” Accuracy scores averaged 48/50 with few hallucinations reported. However, documentation length increased by 30–50 words per note. Burnout scores showed minimal change, but engagement and work-life balance improved in several cohorts. The analysis showed consistent reductions in clinician documentation burden but limited evidence of impact on patient satisfaction or productivity. Secondary analyses using SEIPS 3.0 highlighted improvements in workflow efficiency and user acceptance, while QUEST assessments identified minor safety and reliability concerns. Limitations included small sample sizes, heterogeneous study designs, reliance on self-reported metrics, and lack of external validation. Most studies lacked robust comparisons of transcription accuracy or fairness analyses across subgroups.