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Establishing better workflow with an MR dashboard

Sean Ruck, Contributing Editor | September 22, 2020
Health IT MRI
From the September 2020 issue of HealthCare Business News magazine


The development of the dashboard started at the end of 2016 and it was rolled out for use toward the end of 2018. It was created because the department wanted a mechanism to collect data about their MRs and what delays were occurring in order to share with other departments and management what the biggest barriers were to MR workflow. Prior to the dashboard, that information was somewhat ethereal.
“A lot of the process we were experiencing was verbal or manual, so it wasn’t something that was captured in the information systems we were already using,” said Mueller.

The two had their suspicions about where slowdowns and barriers were, but the information they gathered allowed them to present the information as solid facts. Some of the most useful data weren’t just about showing the barriers, but allowed for trending the slowdowns in order to more clearly address the problems and get additional resources during times of need.

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Michelle Casler
“I would say it also helps us gain targeted information,” said Casler. “We’re able to see what trends exist and use this information to target education on certain units or with certain providers or groups. The data drives decision making, and helps guide policies and procedures, to make the most positive impact on patient care and throughput.”

Casler says that in part, thanks to the two-year development process, the majority of changes and tweaks for the dashboard were made before rollout. Still, she anticipates that updates and changes will be in the mix for the future as new and additional needs are identified.

Mueller offered advice for other professionals looking to introduce their own MR dashboard. “Make sure you define your data elements from the beginning and that everyone is speaking that same language. From a radiology perspective, an MR order, an MR patient, an MR encounter means something very different for someone working in, let’s say for example, ambulatory data. We didn’t necessarily have all of that laid out up front. We had to circle back when we got to the data to iron some of that out. The other thing is that it’s a huge collaboration between clinical and IT people, software developers and data analysts — so making sure you have the right stakeholders at the table is really important.”

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