Kingston, Frontenac, Lennox and Addington Public Health is utilizing a scheduling program, developed by a Queen's-based startup, to help organize its mass immunization effort for COVID-19.

Tasked with immunizing residents throughout its coverage area, KFL&A Public Health faces an immense and complex amount of scheduling involving staff, including full-time, part-time, contract, unionized and non-unionized. To meet the increased needs KFL&A Public Health is using Mesh AI, a cloud-based human resource management software for the healthcare industry that removes the need to manually manage staff work schedules. The business venture, led by Queen’s Engineering Associate Dean (Corporate Relations) and professor in the Department of Electrical and Computer Engineering Shahram Yousefi, offered its software free and with no obligation to healthcare administrators in support of the response to the COVID-19 pandemic back in March of 2020 at the initial peak of the pandemic in North America. Earlier this year, Mesh AI extended its free access offer to new qualifying immunization teams.

The software is currently being used in immunization programs, pandemic surge planning, and hospital as well as medical office staff scheduling in Canada, the United States, and Australia.

Before the pandemic, normal operations for KFL&A Public Health were primarily Monday to Friday 8:30 am-4:30 pm and scheduling was relatively stable, explains Katie Chan, Human Resources Officer for KFLA Public Health. But with the arrival of COVID-19 it became clear that the health unit needed a more sophisticated scheduling system to handle the new requirements.

“Prior to the introduction of Mesh AI scheduling for our organization was a difficult to say the least,” Chan says. “Schedules were being populated manually on Excel and PDF schedules were posted for staff. As you can imagine the upkeep of changes and updates was quite onerous. Mesh AI has provided a seamless system to provide real time data for management and staff.”

Importantly, Chan adds, Mesh AI is user-friendly and public health staff were able to quickly transition into the new system when it was introduced.

“Since then, we have been using Mesh AI for all COVID response units as well as for the on-call schedule for our management team,” she says. “With the upcoming complexity of scheduling the mass immunization clinics given different partners and groups we are grateful to be have access to this new digital tool.”

Introducing new software in healthcare can often take six to 18 months, or more. With a product line designed for COVID-19 immunization staffing, Mesh AI can launch the product for a client in less than two weeks.

“When we launch, the manager presses a button and everybody gets invited to the platform,” Dr. Yousefi explains. “When they come to the platform, they can just input their preferences. This is unique. We allow people to tell the system what they need. If you’re a nurse who is caring for three children, you have specific work-life balance needs. You can put all of that into the system. You might prefer to not have early morning assignments, for example, as you need to help your kids before you can start working. And a single guy who lives as a nurse in downtown Toronto has different needs. So they all log in, put in their requirements, vacation days, off times, on times, and, this is unique to Mesh AI, their preferences. Anything they want. There's no limit. Alternatively, when speed matters, Mesh AI can be launched without the need to on-board all staff. Admins and schedulers, even a single user, can reap the majority of our automation benefits by themselves.”

Once managers receive and approve staffing requests and preferences by their providers or directly add them to Mesh AI themselves, the schedules can be automatically generated. When changes and shift reassignments are required, Mesh AI’s intelligent engine recommends the next best options reducing healthcare administrative times drastically.

Learn more about Mesh AI and MESH Scheduling Inc. at MeshAI.io.

With files from the Faculty of Engineering and Applied Science.