Sponsored Content | Case Study | Information Technology | October 04, 2016

Medical College of Wisconsin Drives Efficiency and Patient Outcomes With Workflow Intelligence

An interview on the ways in which workflows and data can improve performance and patient safety with Greg Holl, radiology informatics system manager at the Medical College of Wisconsin

information technology

Greg Holl

Medical College of Wisconsin provides physician practice at three major affiliates — Froedtert Hospital, Children’s Hospital of Wisconsin and the Zablocki VA Medical Center — and many other hospitals and clinics in the Milwaukee area. Each year, its providers, physician assistants, nurse practitioners and psychologists care for more than 425,000 patients, representing more than 1.6 million patient visits. 

In this article, we talk with Greg Holl, radiology informatics system manager at the Medical College of Wisconsin, about how workflow intelligence software can drive performance improvement and help enhance patient safety in a busy radiology department. 

 

Q: How has workflow orchestration software helped your radiology department and emergency department work more closely together?

A: The radiology department provides “wet reads” and after-hours reads for our emergency department and trauma service. Residents do the preliminary interpretations overnight and meet with staff radiologists in the morning to review their work. The ED was looking for a quality metric to show that the reads provided by our resident trainees are of high quality, and the solution gave us the ability to create this workflow. Conserus Workflow Intelligence also allows us to monitor recall rates and compare them to national averages and we’ve been able to show that our numbers are significantly better than the average. 

 

Q: How are you using this technology?  

A: Peer and resident review are frequently used workflows, but we’ve found that we’re constantly growing with the product and finding new ways to apply it. For example, in addition to the standard peer review workflow, we created discrepancy reviews for educational purposes. We gather and document discrepancies into a database from which we generate reports for stakeholders, which are then used for quality improvement purposes. 

The tool is tightly integrated with our PACS and it was very convenient to configure a workflow that collects data and files this information so we can go back and report on it later. We’re able to document the correlation between the findings to show how often (discrepancies) occur. 

Another situation where we track discrepancies is when an exam is performed at another facility and sent here for a second opinion. Tracking these discrepancies helps us show the value our radiologists bring to the table. 

 

Q: What quality metrics do you track with this solution? 

A: We look at certain quality metrics for compliance purposes, like the participation rates for the American College of Radiology’s (ACR) National Radiology Data Base. We collect the data around participation rates for random review generation, number of received discrepant reviews and other metrics to build a custom report card for our physicians. This report card is a searchable database by physician and date range that is used for periodic reviews with the department chair. Some of the other metrics we collect include relative value unit (RVU) by individual, compared to the section and national averages, report turnaround time for emergency department studies, status of certifications in basic life support, maintenance of certification (MOC), internal and external committee memberships, and more.

 

Q: Has it helped the college and its individual radiologists improve performance? 

A: Participation in peer reviews has increased significantly, with nearly everyone meeting the requirement to randomly review colleagues’ exams within the subspecialty to a rate equal to 2 percent of their own study volumes.  

Q: How have these processes affected peer review and resident review at the college? 

A: The resident trainees asked for a tool that would let them receive feedback on their unattended wet reads. The resident review workflow has fit that need nicely. We are able to route the feedback to the resident for evaluation and it is ready for them when they come back on shift. The feedback is timely and current enough for the resident to still recall the details of the case. The attending radiologist also appreciates the ability to send that feedback without having to note it somewhere and remember to follow up later the next day.

 

Q: Do radiologists view it as a positive tool?

A: We don’t just collect discrepancies, but also use it for positive event reporting for both our radiologists and our technologists. We’re collecting events that had a very positive impact on patient outcomes. We provide accolades to our technologists when they do a really good job with an exam. We can go back and congratulate for a job well done. It really has helped the acceptance of the tool. We don’t want this to be viewed as a punitive system. We want it to be a teaching tool; something that people are supposed to learn and grow from on these reviews. 

 

Q: What additional processes are you planning to your work processes? 

A: We’re looking forward to the ability to fully anonymize reviews — to hide all identifying details on the exam including the author of the report and location of the exam. Right now it’s a very manual process to export images out of the PACS and drop them into a network share, and then redact the identifying information from reports. This process can now be automated with the workflow solution. We can clean up and display the images within the tool. It’s going to save a lot of time.

 

Case study supplied by McKesson Corp.

Related Content

iCAD's ProFound AI Wins Best New Radiology Solution in 2019 MedTech Breakthrough Awards
News | Computer-Aided Detection Software | September 09, 2019
iCAD Inc. announced MedTech Breakthrough, an independent organization that recognizes the top companies and solutions...
A smart algorithm has been trained on a neural network to recognize the appearance of breast cancer in MR images

A smart algorithm has been trained on a neural network to recognize the appearance of breast cancer in MR images. The algorithm, described at the SBI/ACR Breast Imaging Symposium, used deep learning, a form of machine learning, which is a type of artificial intelligence. Image courtesy of Sarah Eskreis-Winkler, M.D.

Feature | Society of Breast Imaging (SBI) | September 06, 2019 | By Greg Freiherr
The use of smart algorithms has the potential to make healthcare more efficient.
Philips and Fujifilm booths at SIIM 2019.

Philips and Fujifilm booths at SIIM 2019.

Feature | SIIM | September 06, 2019 | By Greg Freiherr
Pragmatism from cybersecurity to enterprise imaging was in vogue at the 2019 meeting of the Society of Imaging Inform
Heath information technology diagram showing use of cloud storage.
Feature | Archive Cloud Storage | September 04, 2019 | Tyna Callahan
In healthcare, critical systems are being used to deliver vital information and services 24x7x365.
Global Diagnostics Australia Incorporates AI Into Radiology Applications
News | Artificial Intelligence | September 04, 2019
Global Diagnostics Australia (GDA), a subsidiary of the Integral Diagnostics Group (IDX), has adopted artificial...
The CT scanner might not come with protocols that are adequate for each hospital situation, so at Phoenix Children’s Hospital they designed their own protocols, said Dianna Bardo, M.D., director of body MR and co-director of the 3D Innovation Lab at Phoenix Children’s.

The CT scanner might not come with protocols that are adequate for each hospital situation, so at Phoenix Children’s Hospital they designed their own protocols, said Dianna Bardo, M.D., director of body MR and co-director of the 3D Innovation Lab at Phoenix Children’s.

Sponsored Content | Case Study | Radiation Dose Management | September 04, 2019
Radiation dose management is central to child patient safety. Medical imaging plays an increasing role in the accurate...
New Report Reveals Vulnerabilities of Internet of Things-enabled Healthcare Devices
News | Cybersecurity | August 29, 2019
Use of the Internet of Things (IoT) is booming, with IHS Markit forecasting there will be 73 billion connected devices ...
Royal Solutions and ZipRad Partner to Tackle Order Entry and Pre-authorization
News | Electronic Medical Records (EMR) | August 27, 2019
August 27, 2019 — Medical data delivery company Royal Solutions has partnered with ZipRad to streamline imaging exam
Glassbeam Introduces AI-powered Rules and Alerts Engine for Clinsights
News | Analytics Software | August 23, 2019
Glassbeam Inc. revealed several technology enhancements in its Rules & Alerts engine that make it dramatically...
Sectra Signs Enterprise Imaging Contract With Vanderbilt Health
News | Enterprise Imaging | August 21, 2019
Sectra will install its enterprise imaging picture archiving and communication system (PACS) and vendor neutral archive...