Researchers at Harvard Medical School have developed an automated report monitoring system that eases the task of keeping track of radiology cases for clinical and teaching purposes.
Researchers at Harvard Medical School have developed an automated report monitoring system that eases the task of keeping track of radiology cases for clinical and teaching purposes.
"The system we developed allows users to automate case-tracking for clinical follow-up or teaching purposes," said Chun-Shan Yam, Ph.D., a radiology instructor at Harvard Medical School.
Under the system, radiologists can collect reports for their tracking cases on the fly while dictating. Case-tracking can be initiated by dictating a keyword into the report. Any existing and future reports associated with the same patient will then automatically be collected. Yam described the system in the January issue of the American Journal of Roentgenology.
"Keeping track of radiology cases is time-consuming," he said. "Normally, radiologists must remember the patient's name or ID number, or make a note for tracking, which is why you often find bunches of little papers in radiologists' lab coats."
An automated system can streamline clinical workflow, reduce concerns about Heath Insurance Portability and Accountability Act compliance, and eliminate human error from writing and typing.
Because the report transfer is based on HL7, the only software requirement is the ability to receive HL7 reports. Yam uses commercially available software (LinkTool, LINK Medical Computing) and developed his system on a PC platform.
Any system that can perform TCP/IP communications, including Mac OS, Linux, and Unix, can use the report monitoring method. A number of commercial HL7 products are available on the Internet.
Although this system was initially designed for tracking teaching cases, its flexibility for creating custom functions allows it to also be used for other research activities and daily radiology practices, according to Yam.
"Because the system contains all the RIS reports, both preliminary and final, we are extending it for data mining and quality assurance," he said.
MRI-Based AI Radiomics Model Offers 'Robust' Prediction of Perineural Invasion in Prostate Cancer
July 26th 2024A model that combines MRI-based deep learning radiomics and clinical factors demonstrated an 84.8 percent ROC AUC and a 92.6 percent precision-recall AUC for predicting perineural invasion in prostate cancer cases.
Breast MRI Study Examines Common Factors with False Negatives and False Positives
July 24th 2024The absence of ipsilateral breast hypervascularity is three times more likely to be associated with false-negative findings on breast MRI and non-mass enhancement lesions have a 4.5-fold likelihood of being linked to false-positive results, according to new research.
Can Polyenergetic Reconstruction Help Resolve Streak Artifacts in Photon Counting CT?
July 22nd 2024New research looking at photon-counting computed tomography (PCCT) demonstrated significantly reduced variation and tracheal air density attenuation with polyenergetic reconstruction in contrast to monoenergetic reconstruction on chest CT.