Our Impact on Culture

“We shape our tools and then the tools shape us” — Winston Churchill

When we face challenges at work, the answer is often not technology but good tech can be a tool to get us to where we need to go better and more efficiently.

Mesh AI has been delivering on this very promise since 2014 for a variety of healthcare teams and organizations in Canada, the US, and Australia.

How has Mesh AI helped with improving operations and culture in healthcare?

In scheduling shifts and calls, there are two major ingredients to create success:

1- a quality data set: asking the right questions from clinicians regarding their needs and the reality on the grounds, and,

2- a powerful solver engine: automation to create fair, compliant, and “compassionate” schedules for all taking into accounts rules, constraints, requests, and any other optimization factors required.

Missing any of the two means loss of optimality leading to a number of organizational challenges we are far too familiar with in today’s healthcare.

When schedulers try to fill in all the shifts required and feel like they are dealing with an impossible puzzle, it helps to be able to do the best mathematically possible and make informed decisions that both managers and clinicians understand.

Say you are trying to fill in a 4-week block with 3 clinicians who are only available for one week of service for your unit. It is easy in this simple case to see that this scheduling problem does not have a solution. Some rules need to be broken. Now imagine, having to schedule 100 pediatricians in one unit with many sophisticated rules, latest credentialing information, and demanding vacation and preference requests. It is not easy to convince others when you think there are just not enough people to cover all the calls.

But Mesh AI literally scans millions and if needed hundreds of millions of permutations, combinations, and formations to figure out if solutions exist that satisfy all the constraints and if so it tries to pick the best of those solutions. And here is the added cool power of Mesh AI: when it is not possible to fill all those shifts, it can still break the ‘best rules to break’, fill all the shifts, and inform the planners as to why and what rules were violated. In such cases, Mesh AI can prove to all involved that in fact their scheduling problem is indeed “impossible”.

This has the potential to change how leaders think about and plan their operations. That is exactly what happened in a most recent case, where a chief resident of psychiatry in charge of scheduling a team of 35 fellow residents in Canada was able to prove to her leadership that the way scheduling was being done for close to 12 years was a bad approach that made is often impossible to fill all the gaps. This allowed them to finally reconsidering the process and reducing the load on residents. Mesh AI just managed to move the needle on culture: leadership took feedback from the team (proven by a powerful tool) to improve working conditions for both schedulers and schedulees.