Image interpretation: Learning analytics–informed education opportunities

OBJECTIVES: Using a sample of pediatric chest radiographs (pCXR) taken to rule out pneumonia, we obtained diagnostic interpretations from physicians and used learning analytics to determine the radiographic variables and participant review processes that predicted for an incorrect diagnostic interpretation. METHODS: This was a prospective cross‐sectional study. A convenience sample of frontline physicians with a range of experience levels interpreted 200 pCXR presented using a customized online radiograph presentation platform. Participants were asked to determine absence or presence (with respective location) of pneumonia. The pCXR were categorized for specific image‐based variables potentially associated with interpretation difficulty. We also generated heat maps displaying the locations of diagnostic error among normal pCXR. Finally, we compared image review processes in participants with higher versus lower levels of clinical experience. RESULTS: We enrolled 83 participants (20 medical students, 40 postgraduate trainees, and 23 faculty) and obtained 12,178 case interpretations. Variables that predicted for increased pCXR interpretation difficulty were pneumonia versus no pneumonia (β = 8.7, 95% confidence interval [CI] = 7.4 to 10.0), low versus higher visibility of pneumonia (β = –2.2, 95% CI = –2.7 to –1.7), nonspecific lung pathology (β = 0.9, 95% CI = 0.40 to 1.5), localized versus multifocal pneumonia (β = –0.5, 95% CI = –0.8 to –0.1), and one versus two views (β = 0.9, 95% CI = 0.01 to 1.9). A review of diagnostic errors identified that bony structures, vessels in the perihilar region, peribronchial thickening, and thymus were often mistaken for pneumonia. Participants with lower experience were less accurate when they reviewed one of two available views (p