Medical Teacher最新文献

Background: Nuclear Medicine(NM), as an inherently interdisciplinary field, integrates diverse scientific principles and advanced imaging techniques. The advent of ChatGPT, a large language model, opens new avenues for medical educational innovation. With its advanced natural language processing abilities and complex algorithms, ChatGPT holds the potential to substantially enrich medical education, particularly in NM.

Objective: To investigate the current application of ChatGPT in undergraduate Nuclear Medicine Education(NME).

Methods: Employing a mixed-methods sequential explanatory design, the research investigates the current status of NME, the use of ChatGPT and the attitude towards ChatGPT among teachers and students in the Second Clinical College of Chongqing Medical University.

Results: The investigation yields several salient findings: (1) Students and educators in NM face numerous challenges in the learning process; (2) ChatGPT is found to possess significant applicability and potential benefits in NME; (3) There is a pronounced inclination among respondents to adopt ChatGPT, with a keen interest in its diverse applications within the educational sphere.

Conclusion: ChatGPT has been utilized to address the difficulties faced by undergraduates at Chongqing Medical University in NME, and has been applied in various aspects to assist learning. The findings of this survey may offer some insights into how ChatGPT can be integrated into practical medical education.

Diagnostic error is a significant category within preventable patient harm, and it takes many years of effort to develop proficiency in diagnostic reasoning. One of the key challenges medical schools must address is preparing students for the complexity, uncertainty and clinical responsibility in going from student to doctor. Recognising the importance of both cognitive and systems-related factors in diagnostic accuracy, we designed the QUID Prompt (Questions to Use for Improving Diagnosis) for students to refer to at the bedside. This set of questions prompts careful consideration, analysis, and signposting of decision-making processes, to assist students in transitioning from medical school to the real-world of work and achieving diagnostic excellence in clinical settings.

Background: Case tracking (following-up prior patient encounters) can help inform future clinical decisions and supplement experiential learning. Internal medicine subspecialty fellows see a high volume of patients and need to become subject matter experts within a short time frame, yet little is known about their specific needs and motivations around case tracking.

Objective: The objective of this study was to explore internal medicine subspecialty fellows' motivations, preferences, and practices around case tracking.

Methods: We conducted interviews with internal medicine subspecialty fellows at a single academic medical center during the 2022-2023 academic year. Interviews were analyzed using qualitative content analysis.

Results: 22 fellows were interviewed for our study. We found that most fellows engaged in case tracking with lists in the electronic health record (EHR). Fellows primarily tracked cases of clinical uncertainty and patients with specific diseases or conditions of interest. Fellows sought information on patients' health outcomes, results, and clinical notes. Motivations for tracking were predominantly related to curiosity, professional growth, and development of practice patterns. Barriers to case tracking included time, challenges maintaining patient lists, losing track of patients, and lack of motivation to develop and maintain an organized system.

Conclusions: Internal medicine subspecialty fellows engaged in and valued case tracking as a way to supplement their experiential learning. Our study highlighted their current practices, motivations, preferences, and challenges related to case tracking. We plan to use these findings to help develop an EHR-embedded dashboard to facilitate case tracking among subspecialty fellows.