Accuracy of endobronchial ultrasound (EBUS) in the staging of lung cancer - A comparison of staging EBUS with postoperative pathological nodal staging.

BACKGROUND: Endobronchial ultrasound-guided transbronchial fine-needle aspiration (EBUS-TBNA) has replaced mediastinoscopy as the preferred investigation for evaluating mediastinum in staging lung cancer. There is little evidence of mediastinal staging by EBUS-TBNA from India. OBJECTIVES: To study endobronchial ultrasound's diagnostic accuracy in staging lung cancer. METHODOLOGY: We retrospectively analysed patients operated on for lung cancer where EBUS was performed preoperatively for mediastinal staging. We compared the histological findings obtained from different mediastinal lymph nodes (LNs) by EBUS-TBNA with the pathology of the same LNs obtained after surgical dissection as the reference standard. RESULTS: Seventy-six patients underwent curative surgery for lung cancer. The diagnostic accuracy, sensitivity, specificity, positive predictive value and negative predictive value of EBUS-TBNA in predicting mediastinal metastasis were 93.9%, 40%, 99%, 80% and 94.6%, respectively. Of the 115 LNs sampled, EBUS-TBNA was false negative in six nodes, resulting in an up-staging of six patients. CONCLUSIONS: EBUS-TBNA has a high diagnostic accuracy for lung cancer staging.

AI Education for Fourth-Year Medical Students: Two-Year Experience of a Web-Based, Self-Guided Curriculum and Mixed Methods Study.

BACKGROUND: Artificial intelligence (AI) and machine learning (ML) are poised to have a substantial impact in the health care space. While a plethora of web-based resources exist to teach programming skills and ML model development, there are few introductory curricula specifically tailored to medical students without a background in data science or programming. Programs that do exist are often restricted to a specific specialty. OBJECTIVE: We hypothesized that a 1-month elective for fourth-year medical students, composed of high-quality existing web-based resources and a project-based structure, would empower students to learn about the impact of AI and ML in their chosen specialty and begin contributing to innovation in their field of interest. This study aims to evaluate the success of this elective in improving self-reported confidence scores in AI and ML. The authors also share our curriculum with other educators who may be interested in its adoption. METHODS: This elective was offered in 2 tracks: technical (for students who were already competent programmers) and nontechnical (with no technical prerequisites, focusing on building a conceptual understanding of AI and ML). Students established a conceptual foundation of knowledge using curated web-based resources and relevant research papers, and were then tasked with completing 3 projects in their chosen specialty: a data set analysis, a literature review, and an AI project proposal. The project-based nature of the elective was designed to be self-guided and flexible to each student's interest area and career goals. Students' success was measured by self-reported confidence in AI and ML skills in pre and postsurveys. Qualitative feedback on students' experiences was also collected. RESULTS: This web-based, self-directed elective was offered on a pass-or-fail basis each month to fourth-year students at Emory University School of Medicine beginning in May 2021. As of June 2022, a total of 19 students had successfully completed the elective, representing a wide range of chosen specialties: diagnostic radiology (n=3), general surgery (n=1), internal medicine (n=5), neurology (n=2), obstetrics and gynecology (n=1), ophthalmology (n=1), orthopedic surgery (n=1), otolaryngology (n=2), pathology (n=2), and pediatrics (n=1). Students' self-reported confidence scores for AI and ML rose by 66% after this 1-month elective. In qualitative surveys, students overwhelmingly reported enthusiasm and satisfaction with the course and commented that the self-direction and flexibility and the project-based design of the course were essential. CONCLUSIONS: Course participants were successful in diving deep into applications of AI in their widely-ranging specialties, produced substantial project deliverables, and generally reported satisfaction with their elective experience. The authors are hopeful that a brief, 1-month investment in AI and ML education during medical school will empower this next generation of physicians to pave the way for AI and ML innovation in health care.

Effectiveness of a Vendor Predictive Model for the Risk of Pediatric Asthma Exacerbation: A Difference-in-Differences Analysis.

BACKGROUND: Asthma is a common cause of morbidity and mortality in children. Predictive models may help providers tailor asthma therapies to an individual's exacerbation risk. The effectiveness of asthma risk scores on provider behavior and pediatric asthma outcomes remains unknown. OBJECTIVE: Determine the impact of an electronic health record (EHR) vendor-released model on outcomes for children with asthma. METHODS: The Epic Systems Risk of Pediatric Asthma Exacerbation model was implemented on February 24, 2021, for volunteer pediatric allergy and pulmonology providers as a noninterruptive risk score visible in the patient schedule view. Asthma hospitalizations, emergency department (ED) visits, or oral steroid courses within 90 days of the index visit were compared from February 24, 2019, to February 23, 2022, using a difference-in-differences design with a control group of visits to providers in the same departments. Volunteer providers were interviewed to identify barriers and facilitators to model use. RESULTS: In the intervention group, asthma hospitalizations within 90 days decreased from 1.4% (54/3,842) to 0.7% (14/2,165) after implementation with no significant change in the control group (0.9% [171/19,865] preimplementation to 1.0% [105/10,743] post). ED visits in the intervention group decreased from 5.8% (222/3,842) to 5.5% (118/2,164) but increased from 5.5% (1,099/19,865) to 6.8% (727/10,743) in the control group. The adjusted difference-in-differences estimators for hospitalization, ED visit, and oral steroid outcomes were -0.9% (95% confidence interval [CI]: -1.6 to -0.3), -2.4% (-3.9 to -0.8), and -1.9% (-4.3 to 0.5). In qualitative analysis, providers understood the purpose of the model and felt it was useful to flag high exacerbation risk. Trust in the model was calibrated against providers' own clinical judgement. CONCLUSION: This EHR vendor model implementation was associated with a significant decrease in asthma hospitalization and ED visits within 90 days of pediatric allergy and pulmonology clinic visits, but not oral steroid courses.

Lessons Learned from OpenNotes Learning Mode and Subsequent Implementation across a Pediatric Health System.

BACKGROUND: The 21st Century Cures Act has accelerated adoption of OpenNotes, providing new opportunities for patient and family engagement in their care. However, these regulations present new challenges, particularly for pediatric health systems aiming to improve information sharing while minimizing risks associated with adolescent confidentiality and safety. OBJECTIVE: Describe lessons learned preparing for OpenNotes across a pediatric health system during a 4-month trial period (referred to as "Learning Mode") in which clinical notes were not shared by default but decision support was present describing the upcoming change and physicians could request feedback on complex cases from a multidisciplinary team. METHODS: During Learning Mode (December 3, 2020-March 9, 2021), implementation included (1) educational text at the top of commonly used note types indicating that notes would soon be shared and providing guidance, (2) a new confidential note type, and (3) a mechanism for physicians to elicit feedback from a multidisciplinary OpenNotes working group for complex cases with questions related to OpenNotes. The working group reviewed lessons learned from this period, as well as implementation of OpenNotes from March 10, 2021 to June 30, 2021. RESULTS: During Learning Mode, 779 confidential notes were written across the system. The working group provided feedback on 14 complex cases and also reviewed 7 randomly selected confidential notes. The proportion of physician notes shared with patients increased from 1.3% to 88.4% after default sharing of notes to the patient portal. Key lessons learned included (1) sensitive information was often present in autopopulated elements, differential diagnoses, and supervising physician note attestations; and (2) incorrect reasons were often selected by clinicians for withholding notes but this accuracy improved with new designs. CONCLUSION: While OpenNotes provides an unprecedented opportunity to engage pediatric patients and their families, targeted education and electronic health record designs are needed to mitigate potential harms of inappropriate disclosures.