MDwAIstScheduler: Bringing On-Device Voice Documentation into Clinical Practice

2026-08-15

AI Summary

This work proposes a low-cost, fully on-device wearable voice processing pipeline to mitigate physician burnout and privacy risks associated with clinical documentation. By fine-tuning a 1.7B-parameter clinical language model and integrating medical automatic speech recognition, the system achieves high-precision intent extraction and electronic health record (EHR) draft generation locally, ensuring patient data never leaves the device. This approach eliminates keyboard interaction during consultations and automatically generates structured medical records. Consequently, the proposed system significantly alleviates administrative burdens while strictly preserving data privacy, thereby enhancing physician focus and clinical workflow efficiency without compromising security.

Abstract

Clinical documentation forces physicians to split attention between the patient and their keyboard, and much of it spills into uncom- pensated after-hours work. We present MDwAIstScheduler, a low- cost, belt-worn pipeline that lets a physician speak naturally dur- ing the encounter and have the resulting medications, allergies, labs/orders/referrals, follow-up scheduling, vitals, and problems land in the EHR as review-ready drafts. Building on our earlier prototype, which relied on cloud speech recognition and a cloud language model, the current pipeline runs both transcription and intent extraction entirely on-device. Using a medical-domain auto- matic speech recognition (ASR) model and a 1.7B-parameter lan- guage model we fine-tuned for clinical action extraction, no patient audio or text leaves the device, and the structured drafts are written directly into the Elation EHR for the physician to confirm. The result is a documentation tool that removes keyboard work from the visit without removing the clinician from the record, allowing them to focus on what matters most, patient care, while reducing burden at the same time.

Problem

• Clinical Documentation
• Physician Burnout
• Electronic Health Record
• On-Device AI

Innovation

• On-device AI
• Clinical NLP
• Medical ASR
• Privacy-preserving
• EHR Integration