• AI
  • Molecular Imaging
  • CT
  • X-Ray
  • Ultrasound
  • MRI
  • Facility Management
  • Mammography

Big Brother watches over verification times

Article

With a mix of social engineering and Web-based dashboard management, the radiology department at the University of Maryland Medical System has reduced overall report verification times for its radiologists.

With a mix of social engineering and Web-based dashboard management, the radiology department at the University of Maryland Medical System has reduced overall report verification times for its radiologists.

Even before a report sign-off management system had been implemented, turnaround times decreased as news spread through the department that performance would be monitored, said Paul G. Nagy, Ph.D., an assistant professor of radiology at Maryland.

After two weeks of implementation, the system had reduced report verification times from 32.9 hours to 11.8 hours.

Speech recognition has been touted as a way to improve report turnaround, but it's not for everybody, Nagy said.

Rather than wait for speech recognition to improve, the department chose to optimize its current report workflow, he said during a scientific session at the RSNA meeting. To reduce both verification time for report sign-off and fatigue levels in radiologists, the department developed an in-house system for workflow data management.

The program was developed in the Python programming language. It logs on to the department's RIS four times daily and twice during the weekends to pull all unsigned summary reports. The appropriate radiologists are then paged and e-mailed to notify them that a report is awaiting their sign-off.

When the system was first announced, some radiologists expressed concern that it would represent Big Brother watching over their every move, according to Nagy. But the group developed a metric that, when represented as a cumulative Pareto, would focus on the few laggards responsible for the bulk of the delays.

"It's the law of Pareto - 80% of the problem comes from 20% of people," Nagy said.

After a few weeks, the physicians became reliant on the tool to alert them to reports that needed their attention. They admitted to liking the tool and noted that it was far less intrusive than speech recognition, according to Nagy.

The system has now been in place for over nine months. While there has been some drift in the verification time reduction, turnaround still averages about 16.4 hours, half of what it had been.

For more online information, visit Diagnostic Imaging's RSNA Webcast.

Recent Videos
A Closer Look at the New Appropriate Use Criteria for Brain PET: An Interview with Phillip Kuo, MD, Part 1
Teleradiology and Breast Imaging: Keys to Facilitating Personalized Service, Efficiency and Equity
Current and Emerging Insights on AI in Breast Imaging: An Interview with Mark Traill, MD, Part 3
Current and Emerging Insights on AI in Breast Imaging: An Interview with Mark Traill, MD, Part 2
Current and Emerging Insights on AI in Breast Imaging: An Interview with Mark Traill, Part 1
Addressing Cybersecurity Issues in Radiology
Computed Tomography Study Shows Emergence of Silicosis in Engineered Stone Countertop Workers
Can an Emerging AI Software for DBT Help Reduce Disparities in Breast Cancer Screening?
Related Content
© 2025 MJH Life Sciences

All rights reserved.