Parental Education for the Prevention of Plagiocephaly.

INTRODUCTION: The American Academy of Pediatrics Back-to-Sleep Campaign significantly reduced infant mortality from sudden infant death syndrome. As a result of prolonged supine positioning, the incidence of deformational plagiocephaly has also risen 5-fold since its adoption. We aimed to improve the current educational paradigm for new parents with the goal of reducing the incidence of plagiocephaly within the confines of the Back-to-Sleep Campaign. We hypothesized that the early addition of plagiocephaly focused education for parents would reduce cephalic index, the ratio of head width to length, used as an easily measured objective proxy for positional plagiocephaly. METHODS: Children were screened at their newborn visit. Premature newborns and those diagnosed with craniofacial disorders were excluded. For those enrolled, biparietal and anteroposterior measurements of the head were obtained using manual calipers to obtain cephalic index. Subjects randomly assigned to the intervention group were shown a 2-minute video and given an educational pamphlet on methods to prevent plagiocephaly. Unpaired 2-sample t tests comparing mean differences in intervention and control were performed. RESULTS: Thirty-nine subjects were enrolled as of November 2023 with variable lengths of follow-up completed. The average baseline cephalic index for subjects in the control group was 82.7 and 83.8 for intervention group. Unpaired 2-sample t tests were performed at 2-, 4-, and 6-month time points to analyze the difference between groups. At 4 months, average cephalic index for subjects in the control and treatment group, respectively, was 90.6 and 83.4 (P = 0.02). SIGNIFICANCE: Parental education at the newborn visit led to decreases in cephalic index, a proxy for positional plagiocephaly, compared with control patients. This simple intervention has the potential to reduce parental stress and healthcare costs associated with the evaluation and treatment of plagiocephaly.

Cross-Check QA: A Quality Assurance Workflow to Prevent Missed Diagnoses by Alerting Inadvertent Discordance Between the Radiologist and Artificial Intelligence in the Interpretation of High-Acuity CT Scans.

PURPOSE: The aim of this study was to implement and evaluate a quality assurance (QA) workflow that leverages natural language processing to rapidly resolve inadvertent discordance between radiologists and an artificial intelligence (AI) decision support system (DSS) in the interpretation of high-acuity CT studies when the radiologist does not engage with AI DSS output. METHODS: All consecutive high-acuity adult CT examinations performed in a health system between March 1, 2020, and September 20, 2022, were interpreted alongside an AI DSS (Aidoc) for intracranial hemorrhage, cervical spine fracture, and pulmonary embolus. CT studies were flagged for this QA workflow if they met three criteria: (1) negative results by radiologist report, (2) a high probability of positive results by the AI DSS, and (3) unviewed AI DSS output. In these cases, an automated e-mail notification was sent to our quality team. If discordance was confirmed on secondary review-an initially missed diagnosis-addendum and communication documentation was performed. RESULTS: Of 111,674 high-acuity CT examinations interpreted alongside the AI DSS over this 2.5-year time period, the frequency of missed diagnoses (intracranial hemorrhage, pulmonary embolus, and cervical spine fracture) uncovered by this workflow was 0.02% (n = 26). Of 12,412 CT studies prioritized as depicting positive findings by the AI DSS, 0.4% (n = 46) were discordant, unengaged, and flagged for QA. Among these discordant cases, 57% (26 of 46) were determined to be true positives. Addendum and communication documentation was performed within 24 hours of the initial report signing in 85% of these cases. CONCLUSIONS: Inadvertent discordance between radiologists and the AI DSS occurred in a small number of cases. This QA workflow leveraged natural language processing to rapidly detect, notify, and resolve these discrepancies and prevent potential missed diagnoses.

Diversity, Equity, and Inclusion Programs in Radiology: Data-Driven Strategies for Success, From the AJR Special Series on DEI.

Radiology practices nationwide have implemented diversity, equity, and inclusion (DEI) programs seeking to change the landscape of a field traditionally dominated by White men. However, measuring these programs' success is often challenging, and a lack of documented success may threaten such programs' ongoing growth. This article explores the application of metrics and overall concepts from the business world to assess the success of DEI programs in radiology practices. Metrics and processes are described for each area, including demographic data for measuring diversity, compensation and leadership representation data for measuring equity, and survey-based data for measuring inclusion. A practical data-driven framework is presented that radiology practices may adapt to their DEI programs. This framework is centered on critical assessment of a practice's current shortcomings and creation of leadership accountability to track progress and effect real change. Through the spectrum of considerations highlighted in this article, DEI programs can have a large impact in driving measurable success for radiology practices and ultimately the specialty at large.