Beyond the Case Presentation: Leveraging Peer Learning to Achieve Systems Improvements in Multiple Sclerosis (MS) Care

Purpose: Cases reviewed in our Peer Learning program revealed opportunity for improved imaging and interpretation of MRI performed for assessment of multiple sclerosis (MS). Our aim to improve our MS imaging arose from identification the published benefits of new sequences, desire to standardize imaging across scanners, preference for standardized interpretation and reporting, and recent clinician inquiry about the use of artificial intelligence (AI) algorithms.

Methods/Materials: We leveraged the attendance and engagement of our CME-accredited, quarterly Peer Learning conference to drive disease-specific quality improvement in MRI imaging. At successive Peer Learning conferences, we reviewed currently published guidelines, shared cases, and introduced incremental changes. For workup of cases of “possible MS”, we established a dedicated 3T brain protocol, including a DIR sequence to increase conspicuity of cortical lesions and hi-resolution SWI imaging to optimize detection of a central vein sign. For routine MS brain and spine imaging, we optimized and standardized sequences across scanners according to current published recommendations. For imaging interpretation and reporting, we reviewed discrepant cases and developed a standardized dictation template. Lastly, we arrived at a consensus that AI algorithms are promising, but not validated to the extent needed for our current practice.

Results: Following the revisions of our routine MS brain and spine imaging, we collected feedback from 7 neuroradiologists and 1 neurologist on overall imaging quality, conspicuity of individual lesions, and confidence in lesion detection using a 5-part Likert scale. Regarding the new protocol, 85.7% preferred the image quality, 89.3% agreed lesions were more conspicuous, and 93.3% expressed increased confidence in lesion detection. 89.3% of responses favored the new protocol both for MS diagnosis and surveillance.

Conclusions: We describe our multistep, collaborative approach to achieving disease-specific quality improvement in MS imaging by leveraging the Peer Learning Conference.