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Maintaining Data Integrity: Ann Monks, Yale New Haven Health
Ann Monks, Director of Facilities Data and Technology Analytics at Yale New Haven Health, shares how her team uses CMS Analytics to maintain accurate data.
Data integrity is central to a healthcare system on the road to long term financial planning success. Without it, incomplete or inaccurate data can lead to misleading reporting that can cost a hospital during inspections and accreditation or lead to insufficient capital funds allocated to operations.
In FSI’s latest webinar, Ann Monks, Director of Data and Technology Analytics at Yale New Haven Health, shared her insights into why healthcare systems should take steps to protect data integrity, how data is used for high level decision making, and much more.
How does facilities data factor into high-level decision making?
At Yale New Haven Health, we are a data driven organization, so if you're making any kind of request, whether that be for capital or personnel, you better have the data to back it up. Without the data you're just another person with an opinion, and that doesn't get you very far anymore.
It's also rare that you find a CFO who truly understands what it takes to keep a healthcare facility operational. I can guarantee that your CFO knows how to read a chart, though, so having your facilities data presented in a way that makes sense to everyone in the room is key.
The data ends up speaking for itself. Luckily here at Yale New Haven, we have been using CMS Analytics for almost 4 years now and we have our facilities data organized in a way that allows us to share it with the decision makers in our organization.
How does CMS help ensure data integrity?
CMS Analytics in itself helps tremendously with data integrity. If something is off or a report is showing inconsistencies, CMS Analytics is going to make that clear. You're also able to drill down and find the issue so it can be corrected.
A recent example is how our technicians were tracking their time for work orders in CMS, and how that time translated into our productivity dashboard. We noticed very high daily averages, and when we began to drill down into the data, it became obvious that specific technicians were putting all of their hours for the whole week into one day. They sat down at the end of the week to log their time, but they did not spread it accordingly throughout the week. Some technicians entered their hours properly, but we need everyone to do this to really make that data meaningful.
Until the data is entered correctly, the dashboard can’t tell us much, other than the exact technicians that require additional training, which in the end is exactly what we need to know to improve our overall data integrity.
How do you integrate your data standards to existing sites that join the Yale New Haven Health enterprise?
It's definitely a process, but one that we luckily have some experience in at this point.
It all starts with a standard set of locations, which we currently receive via integration from our space management system. We start with that core location list. When we do not have a good asset inventory for a campus that's coming online, we've started from scratch and we've had FSI go through and barcode every asset, enter it into this system, while following our Yale New Haven Health standard nomenclature, which aligns with Uniformat.
We've standardized as much as we can across our portfolio, including procedures which live in our shared segment, where all other segments are able to pull from. Any changes to these procedures occur in the shared segment and trickle down to the other segments using them.
Do you have any advice for helping "non data people" understand and use the metrics valuable to their work without getting overwhelmed?
I would say start with the basics. Individuals who are less familiar with dashboards and analytics may benefit from you just explaining what type of chart you're looking at, why it's being used, and how it works to tell a story.
I think also sharing the underlying data that's being used to feed the graphs and charts is important because often seeing the data in that tabular form tends to resonate with more people. Then once they understand what data is being used, going back to those graphics to see how that data is embodied on the graph. It begins to make more sense.
What would you tell other healthcare systems struggling with incomplete and inaccurate data?
It's an ongoing challenge to maintain data integrity. You could be at 100% integrity one day, and then the next day you're not. You need to put the processes in place to ensure that the data is getting reviewed for completeness, and doing this at a regular cadence. I think it also needs to be part of the culture for the organization that the data is getting analyzed and used.
Often it's not so obvious to everyone that the outcome of what we are able to do with the data is only going to benefit the individuals that are asking to enter it in. If leadership is trying to make the case that we need additional FTEs [full time employee], well it's hard to provide backup for that when the data and the dashboard doesn't make any sense because it's not complete, or inaccurate.
Transparency overall is key, and then I think explaining how the data is being used really goes a long way.