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Currently submitted to: JMIR Medical Informatics

Date Submitted: Jun 23, 2026
Open Peer Review Period: Jul 16, 2026 - Sep 10, 2026
(currently open for review)

Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

An Open-Source Modular Platform for AI-Enabled Clinical Documentation in Emergency Medicine (Berta): Development and Implementation Study

  • Samridhi Vaid; 
  • Mike Weldon; 
  • Jesse Dunn; 
  • Sacha Davis; 
  • Kevin Lonergan; 
  • Henry Li; 
  • Jeffrey Franc; 
  • Mohamed Abdalla; 
  • Daniel C. Baumgart; 
  • Jake Hayward; 
  • J Ross Mitchell

ABSTRACT

Background:

Clinical documentation is a major contributor to physician workload and burnout, particularly in high-acuity settings such as emergency departments. Ambient artificial intelligence (AI) scribes have emerged as a response, but commercial products cost US $99 to $600 per physician per month, operate as opaque systems, and do not return data to institutional infrastructure. These characteristics limit organizational control over data governance, quality improvement, and clinical workflows, and create barriers to adoption in resource-constrained settings

Objective:

To develop an open-source, modular AI scribe platform deployable entirely within institutional infrastructure, and to evaluate its real-world use across a provincial health system.

Methods:

We developed Berta, an open-source modular platform that pairs a Next.js front-end and a FastAPI backend with configurable automatic speech recognition and large language model components. A customized implementation was deployed at Alberta Health Services (AHS), Canada’s largest provincially integrated health system at the time of implementation, integrated with AHS’s existing Snowflake AI Data Cloud infrastructure. All clinical audio and text remained within the secure AHS environment. Usage and session metadata were automatically recorded during routine clinical use by emergency physicians across AHS between November 2024 and July 2025. Operating costs were estimated from Snowflake service consumption over the same period.

Results:

During 8 months, 198 emergency physicians used the system in 105 urban and rural facilities, generating 22,148 clinical sessions and more than 2800 hours of audio. Monthly volume grew from 680 sessions in November 2024 to 5530 in July 2025. Mean recorded session length was 7.6 minutes, and 42% of users customized at least one documentation template. Operating costs averaged less than US $30 per physician per month, a 70% to 95% reduction compared with commercial alternatives. Based on pilot outcomes, AHS has approved expansion to all provincial acute care physicians

Conclusions:

An open-source AI scribe can be deployed at provincial scale within existing health system infrastructure at a fraction of the cost of commercial alternatives, while preserving data sovereignty and institutional control. By releasing Berta publicly, we provide a reproducible foundation deploy AI documentation technology within their own secure environments.


 Citation

Please cite as:

Vaid S, Weldon M, Dunn J, Davis S, Lonergan K, Li H, Franc J, Abdalla M, Baumgart DC, Hayward J, Mitchell JR

An Open-Source Modular Platform for AI-Enabled Clinical Documentation in Emergency Medicine (Berta): Development and Implementation Study

JMIR Preprints. 23/06/2026:105385

DOI: 10.2196/preprints.105385

URL: https://preprints.jmir.org/preprint/105385

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