Enhancing efficiency and care with AI-powered document sorting for clinics

Michael Yingbull, School of Applied Computer Science & IT

In Canada’s medical clinics, managing the steady flow of incoming electronic documents is a significant, time-intensive task. From health data to faxed reports, each document requires manual sorting and tagging, a process that is not only slow but also costly. While commercial AI solutions are emerging to tackle this issue, they tend to be closed-source, cloud-based systems that raise data security concerns and lack compatibility with widely used open-source EMR platforms like OSCAR. Addressing these gaps, Spring Medical Corp. sought to develop a secure, open-source AI solution that could integrate seamlessly into OSCAR-based EMR systems, ensuring control over patient information while boosting operational efficiency at their Get Well Clinic in North York, Toronto. 

The impact of this project extends well beyond efficiency. By reducing the time spent on administrative tasks, Spring Medical Corp. can significantly lower operating costs, laying the foundation for clinic expansion and service improvements. With the projected cost savings and operational efficiency, Spring Medical Corp. plans to reinvest in healthcare provision, hiring additional medical staff and broadening patient care options. As well, by releasing this tool as an open-source solution, the project’s benefits can extend nationwide, offering substantial cost savings and workflow enhancements to thousands of clinics across Canada. 

We acknowledge the support of the Natural Sciences and Engineering Research Council of Canada (NSERC).