Detection of Critical Spinal Epidural Lesions on CT Using Machine Learning.

BACKGROUND: Critical spinal epidural pathologies can cause paralysis or death if untreated. Although magnetic resonance imaging is the preferred modality for visualizing these pathologies, computed tomography (CT) occurs far more commonly than magnetic resonance imaging in the clinical setting. OBJECTIVE: A machine learning model was developed to screen for critical epidural lesions on CT images at a large-scale teleradiology practice. This model has utility for both worklist prioritization of emergent studies and identifying missed findings. MATERIALS AND METHODS: There were 153 studies with epidural lesions available for training. These lesions were segmented and used to train a machine learning model. A test data set was also created using previously missed epidural lesions. The trained model was then integrated into a teleradiology workflow for 90 days. Studies were sent to secondary manual review if the model detected an epidural lesion but none was mentioned in the clinical report. RESULTS: The model correctly identified 50.0% of epidural lesions in the test data set with 99.0% specificity. For prospective data, the model correctly prioritized 66.7% of the 18 epidural lesions diagnosed on the initial read with 98.9% specificity. There were 2.0 studies flagged for potential missed findings per day, and 17 missed epidural lesions were found during a 90-day time period. These results suggest almost half of critical spinal epidural lesions visible on CT imaging are being missed on initial diagnosis. CONCLUSION: A machine learning model for identifying spinal epidural hematomas and abscesses on CT can be implemented in a clinical workflow.

Long-term solution to malpractice crises: reduce harm to patients.

Physician executives need to lead the charge and actually reduce harm to patients. Simply capping malpractice awards or changing the rules of litigation is not enough.

Pittsburgh Regional Healthcare Initiative: a systems approach for achieving perfect patient care.

The Pittsburgh Regional Healthcare Initiative (PRHI) is an innovative model for health system change based on regionwide shared learning. By linking patient outcomes data with processes of care and sharing that information widely, PRHI supports measurable improvements in regionwide clinical practice and patient safety. In addition, through the redesign of problem solving at the front lines of care, PRHI helps health care organizations to evolve toward becoming sustainable systems of perfect patient care. This paper describes PRHI's design for change, reviews the progress and limitations of the shared learning model, and offers a set of broader policy considerations.