Quality control (QC) is most effective when it is continuously conducted at the point of service delivery and when a mechanism for communicating the quality analysis is available. In the digital diagnostic image PACS environment, one of the greatest barriers to continuous QC is the lack of established communication between those who interpret and acquire images, according to Paul Nagy, PhD.
Nagy and a team of developers from the department of radiology at the University of Maryland Medical School in Baltimore created a PACS-integrated QC application using open-source software tools. He reported the results of their efforts at the 93rd scientific assembly and annual meeting of the Radiological Society of North America last month in Chicago.
“We developed a web-based reporting tool, Radtracker, to track quality control issues identified by the radiologist and integrated this tool into the PACS to be a seamless part of clinical workflow,” Nagy said.
He reported that Radtracker was developed using the PHP hypertext preprocessing script language and interpreter and a MySQL open-source database. After deployment of the tool, the department’s PACS was then able to launch the Radtracker web site and pass it variables for the accession number, medical record number, modality, and radiologist submitting the issue.
The QC issues reported to Radtracker are automatically sent to a modality supervisor as text pages or as e-mails. When a QC issue is resolved, an e-mail is automatically sent to the original submitter.
Nagy said that each issue is assigned a root cause and, if appropriate, to a specific technologist. If the study in question had a previous associated QC issue submitted, a separate web page displays all actions taken to date and whether the issue had been resolved.
According to Nagy, integration with the PACS made issue submission a two-click process, and resulted in more QC matters being brought to the attention of the department’s technologists.
He reported that an average of 185 issues was submitted each month for the next six months. In comparison, for the three months prior to the deployment of Radtracker, a total of 45 QC issues were submitted.
Nagy said that the modalities with the most QC issues were CR/DR (69 percent), CT (21 percent), and MRI (3 percent). The three most common causes for QC issues were problems with image quality (25 percent), patient data (25 percent), and PACS functionality (8 percent), Nagy said.
“This automated feedback can provide a vital link in improving technologist performance and diagnostic accuracy in an environment of increasing workloads,” he said.
Nagy noted that the quality control database of issues can be used for conferences as a learning tool. In addition, because Radtracker is data driven and specific, it can be utilized as part of the performance appraisal process of technologists.
“Lowering the barriers for radiologist access to QC exception reporting dramatically increased the submission of QC issues,” he said. “Strong feedback loops ensure good communication between the radiologists and the technologists.”