Breaking Boundaries: AI-Driven Documentation at KidsAbility

Currently, KidsAbility therapists spend a substantial portion of their time on indirect client activities, primarily focused on creating extensive reports and notes that detail each child’s progress. For every minute spent with a client, clinicians spend up to two minutes writing these important documents. This process, while crucial, is time-consuming and diverts valuable resources from direct care.

Through this project, we will implement a Large Language Model (LLM) that has been trained to analyze therapists’ notes and observations and autonomously turn them into detailed assessment reports and progress notes for the clinician’s review. This AI aid is designed to efficiently streamline documentation tasks while maintaining the high-quality reports expected by families, clinicians, and other stakeholders.

By introducing this AI into their workflows, KidsAbility aims to significantly reduce the time therapists spend on indirect care, allowing them to reallocate their time to treating more clients. This project stands as a testament to KidsAbility’s commitment to using technology to serve more clients and reduce wait times.

Faculty Supervisor:

Bryan Tripp

Student:

Partner:

KidsAbility

Discipline:

Engineering

Sector:

Health and Related Sciences & Technology

University:

University of Waterloo

Program: