Human oversight on phiz

AI Scribes in Public Health: Reimagining Workflows Around Care

Tue, 28 Apr 2026 00:00:00 -0400

Introduction #

If you’ve worked in public health, you already know this feeling.

A single client interaction rarely stays in one place. Notes are written, then rewritten. Information is entered into one EMR (Electronic Medical Record) system, then entered again into another (e.g., iPHIS). Investigation forms, whether developed by Public Health Ontario (PHO) or by local public health units for specific diseases, must also be completed. What begins as a conversation can become a series of fragmented entries across platforms, pulling staff time and attention away from listening, care planning, and supporting people and communities.

AI scribes can help shift that attention back.

Explainable AI for Public Health: Client Linkage and Deduplication

Tue, 03 Feb 2026 00:00:00 -0500

Public health data is messy in very human ways. Families share phones and email addresses. Clinics rely on placeholder dates so work can keep moving. Organizations sometimes appear where people should. Any patient deduplication approach that ignores these realities will struggle to produce results teams can trust.

Probabilistic deduplication and linkage is a key public health AI use case that supports responsible stewardship of personal health information. Using machine learning, it estimates match likelihood across records that are often incomplete, transcription-heavy, and not consistently validated against authoritative sources. We treat it as governed, explainable decision support aligned with our responsibilities as a health information custodian (HIC), with equity considerations and routine human review.

Named Entity Recognition for Redaction & Warning

Mon, 05 Feb 2024 00:00:00 -0500

Probabilistic Client Linkage & Deduplication Consultations

Thu, 25 Jan 2024 00:00:00 -0500