JMIR Publications

ABSTRACT

Background:

Medical documentation imposes a significant administrative burden on physicians, reducing time for patient interaction and increasing the risk of burnout.1 AI-assisted solutions have shown promise in easing this burden, but their effectiveness in multilingual healthcare settings remains underexplored. Switzerland’s linguistic diversity presents unique documentation challenges, particularly for non-native speakers who must navigate multiple languages and dialects.

Objective:

To compare the efficiency and documentation quality of four medical 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 < 0.001). For the non-native speaker, workflow 4 was not significantly faster than traditional dictation (workflow 1) after adjustment (P = 0.077). 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 < 0.001), 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.