Imagine turning minutes of downtime during the workday into valuable CME credits. Researchers at the Medical College of Wisconsin have taken advantage of information technology to develop an electronic learning environment that provides radiological education on demand.
Imagine turning minutes of downtime during the workday into valuable CME credits. Researchers at the Medical College of Wisconsin have taken advantage of information technology to develop an electronic learning environment that provides radiological education on demand.
"What we wanted to do was try and deliver educational 'intervention' at the moment it is needed," said lead author Dr. Charles E. Kahn, a professor of radiology and medical informatics at the college.
Describing the architecture of the educational environment during an RSNA informatics session, Kahn outlined the system's four main components:
* Curriculum - allows the learner to define relevant educational materials and to monitor educational progress.
Presentation - displays learning modules based on the curriculum and clinical context.
* Assessment - scores the user's responses for self-assessment purposes.
* Feedback - allows users to provide feedback evaluating the quality of educational materials.
To plug into a clinical context, an e-learning system must be integrated with radiology information systems, PACS, and clinical decision support systems, Kahn said. To facilitate this level of integration, investigators used the clinical context object workgroup (CCOW) standard. The standard is vendor-independent and provides context management for the system.
The ultimate goal of the system is to deliver content in relatively small nuggets. Because of its assessment capability, it not only can deliver self-evaluation for system users but can also measure how the additional education is actually affecting patient outcomes, Kahn said.
An audience member asked how on-demand learning, even if it is only for 10 or 20 minutes, can fit into already-overworked radiologists' workflows.
Kahn responded that radiologists already engage in daily learning when they encounter clinical questions for which they don't have answers, but they call it decision support. This system is simply an extension of that process.
The just-in-time learning model will be more of a complement to traditional CME meetings than a substitute for them, he said. The Medical College of Wisconsin plans to implement its system in a PACS/RIS environment within six to 12 months.
Study Reaffirms Low Risk for csPCa with Biopsy Omission After Negative Prostate MRI
December 19th 2024In a new study involving nearly 600 biopsy-naïve men, researchers found that only 4 percent of those with negative prostate MRI had clinically significant prostate cancer after three years of active monitoring.
Study Examines Impact of Deep Learning on Fast MRI Protocols for Knee Pain
December 17th 2024Ten-minute and five-minute knee MRI exams with compressed sequences facilitated by deep learning offered nearly equivalent sensitivity and specificity as an 18-minute conventional MRI knee exam, according to research presented recently at the RSNA conference.
Can Radiomics Bolster Low-Dose CT Prognostic Assessment for High-Risk Lung Adenocarcinoma?
December 16th 2024A CT-based radiomic model offered over 10 percent higher specificity and positive predictive value for high-risk lung adenocarcinoma in comparison to a radiographic model, according to external validation testing in a recent study.