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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

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Integrating AI Scribe Technology into Public Health Workflows: Simulation and Field Evaluation in Disease Case

Nauman Shakeel¹, Natalie Riewe², Steven Rebellato²

¹University of Waterloo, Ontario, Canada.

Studies in Health Technology and Informatics

|May 23, 2026

Summary

Generative artificial intelligence (AI) scribes can reduce administrative tasks in public health surveillance. This pilot study found AI scribes improved efficiency but faced challenges in deployment and standardization.

Keywords:

AI Scribe Ambient AI artificial intelligence (AI)case report documentation public health informatics public health surveillance

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Published on: May 15, 2020

Area of Science:

Public Health
Artificial Intelligence
Health Informatics

Background:

Generative artificial intelligence (GenAI) integration into clinical documentation presents opportunities for public health surveillance and response.
AI-scribe technology is increasingly explored for clinical settings, but its application in public health surveillance is less understood.

Purpose of the Study:

To evaluate the effectiveness and usability of an AI scribe for infectious disease management within Public Health Units (PHUs).
To assess the impact of AI scribes on administrative workload and documentation time in public health settings.

Main Methods:

A two-phase pilot study was conducted in two Public Health Units (PHUs) in Ontario, Canada.
The evaluation involved assessing AI scribe performance in capturing relevant information, filtering irrelevancies, and measuring documentation time.
User perceptions of usability and efficiency were also collected.

Main Results:

The AI scribe demonstrated meaningful reductions in administrative workload and positive user perceptions of usability and efficiency in simulation.
Documentation time reductions were observed at one PHU (12.9 minutes, p < 0.05), while an increase was noted at another (10.4 minutes, p < 0.05).
Key challenges included technical deployment issues, generating standardized PHU-specific notes, and establishing trust in AI-generated documentation.

Conclusions:

AI scribes show potential for enhancing infectious disease surveillance by improving documentation efficiency.
Addressing challenges related to deployment, standardization, and trust is crucial for broader adoption.
Future research should focus on scaling AI scribe platforms and integrating outputs with public health information systems for data-driven responses.