JMIR Publications

ABSTRACT

Background:

Medical documentation imposes a significant administrative burden on physicians and reduces time for direct patient care. Artificial intelligence (AI)-assisted tools such as automatic speech recognition and large language models (LLMs) promise to reduce this burden, but their performance in multilingual environments has not been explored. Switzerland is highly multilingual, and non-native German-speaking physicians may find documentation particularly challenging.

Objective:

To compare the efficiency and documentation quality of four clinical documentation workflows—including both AI-assisted and traditional methods—in a Swiss tertiary hospital setting characterized by linguistic diversity.

Methods:

In this proof-of-concept study at a Swiss tertiary hospital (Department of Plastic and Hand Surgery, Cantonal Hospital Aarau), two physicians—a native Swiss German speaker and a non-native German speaker—documented encounters with simulated patients having common hand disorders. Four documentation workflows were tested: (1) traditional dictation with transcription by a secretary; (2) real-time dictation using speech recognition software for voice to text transcription; (3) post-encounter dictation transcribed by an AI (Whisper) and processed by a GPT-based agent; and (4) AI-assisted ambient dictation of entire appointments using audio recording and automatic transcription. Documentation efficiency was measured by recorded physician time, and note quality was assessed using a modified Physician Documentation Quality Instrument (PDQI-9) scored by three different large language models (LLMs). To protect patient privacy, only synthetic (simulated) patient data were used.

Results:

AI-assisted workflows—particularly workflow 4 (AI-assisted ambient dictation)—produced the shortest physician documentation times per report. In post-hoc comparisons, workflow 4 was significantly faster than solely the speech recognition software workflow (workflow 2) for both physicians (adjusted P < .001). For the non-native speaker, workflow 4 was not significantly faster than traditional dictation (workflow 1) after adjustment (P = .077). All workflows achieved uniformly high documentation quality scores (median PDQI-9 total score 47.3/50). A Friedman test indicated a statistically significant difference in quality scores between workflows (χ² = 36, P < .001), but the absolute score differences were <1 point, which is not clinically meaningful.

Conclusions:

AI-assisted documentation—especially ambient AI dictation—has significant potential to streamline medical documentation in multilingual healthcare settings without compromising quality. Such tools can help alleviate the linguistic challenges faced by non-native speakers, reduce administrative burdens, and enable physicians to spend more time with patients. Future studies should evaluate these workflows in real-world clinical implementation, address data privacy and security issues, and include human evaluators to validate the benefits observed in this study.