BackgroundThe transition from medical student to hospital-based first year junior doctor (termed “intern” in Australia) is known to be challenging, and recent changes in clinical learning environments may reduce graduate preparedness for the intern workplace. Although manageable challenges and transitions are a stimulus to learning, levels of burnout in junior medical colleagues are concerning. In order to prepare and support medical graduates, educators need to understand contemporary junior doctor perspectives on this transition.MethodsFinal-year University of Queensland medical students recruited junior doctors working in diverse hospital settings, and videorecorded individual semi-structured interviews about their transition from medical student to working as a junior doctor. Two clinical academics (NS and JT) and an intern (ZT) independently conducted a descriptive analysis of interview transcripts, and identified preliminary emerging concepts and themes, before reaching agreement by consensus on the major overarching themes.ResultsThree key themes emerged from the analysis of 15 interviews: internship as a “steep learning curve”; relationships and team; and seeking help. Participants described the intern transition as physically, mentally and emotionally exhausting. They learned to manage long days, administrative and clinical tasks, frequent interruptions and time pressures; identify priorities; deal with criticism without compromising key relationships; communicate succinctly; understand team roles (including their own status within hospital hierarchies); and negotiate conflict. Participants reported a drop in self-confidence, and difficulty maintaining self-care and social relationships. Although participants emphasised the importance of escalating concerns and seeking help to manage patients, they appeared more reluctant to seek help for personal issues and reported a number of barriers to doing so.ConclusionFindings may assist educators in refining their intern preparation and intern training curricula, and ensuring that medical school and intern preparation priorities are not seen as competing. Insights from non-medical disciplines into the organisational and relational challenges facing junior doctors and their health-care teams may enhance inter-professional learning opportunities. Workplace support and teaching, especially from junior colleagues, is highly valued during the demanding intern transition.

Purpose: This study describes the initial development of a deep learning algorithm, ROP.AI, to automatically diagnose retinopathy of prematurity (ROP) plus disease in fundal images.Methods: ROP.AI was trained using 6974 fundal images from Australasian image databases. Each image was given a diagnosis as part of real-world routine ROP screening and classified as normal or plus disease. The algorithm was trained using 80% of the images and validated against the remaining 20% within a hold-out test set. Performance in diagnosing plus disease was evaluated against an external set of 90 images. Performance in detecting pre-plus disease was also tested. As a screening tool, the algorithm's operating point was optimized for sensitivity and negative predictive value, and its performance reevaluated.Results: For plus disease diagnosis within the 20% hold-out test set, the algorithm achieved a 96.6% sensitivity, 98.0% specificity, and 97.3% 6 0.7% accuracy. Area under the receiver operating characteristic curve was 0.993. Within the independent test set, the algorithm achieved a 93.9% sensitivity, 80.7% specificity, and 95.8% negative predictive value. For detection of pre-plus and plus disease, the algorithm achieved 81.4% sensitivity, 80.7% specificity, and 80.7% negative predictive value. Following the identification of an optimized operating point, the algorithm diagnosed plus disease with a 97.0% sensitivity and 97.8% negative predictive value.Conclusions: ROP.AI is a deep learning algorithm able to automatically diagnose ROP plus disease with high sensitivity and negative predictive value.Translational Relevance: In the context of increasing global disease burden, future development may improve access to ROP diagnosis and care.