Informatics in Undergraduate Medical Education: Analysis of Competency Frameworks and Practices Across North America

Design, implementation and evaluation of a spiral module combining data science, digital health and evidence-based medicine in the undergraduate medical curriculum: A mixed methods study

BACKGROUND

Digital health, data science and health informatics are increasingly important in health and healthcare, but largely ignored in undergraduate medical training.

METHODS

In a large UK medical school, with staff and students, we co-designed a new, 'spiral' module (with iterative revisiting of content), covering data science, digital health and evidence-based medicine, implementing in September 2019 in all year groups with continuous evaluation and improvement until 2022.

RESULTS

In 2018/19, a new module, 'Doctor as Data Scientist', was co-designed by academic staff (n = 14), students (n = 23), and doctors (n = 7). The module involves 22 staff, 120 h (43 sessions: 22 lectures, 15 group and six other) over a 5-year curriculum. Since September 2019, 5,200 students have been taught with good attendance. Module student satisfaction ratings were 92%, 84%, 84% and 81% in 2019, 2020, 2021 and 2022 respectively, compared to the overall course (81%).

CONCLUSIONS

We designed, implemented and evaluated a new undergraduate medical curriculum that combined data science and digital health with high student satisfaction ratings.

The Role of Large Language Models in Medical Education: Applications and Implications

Large language models (LLMs) such as ChatGPT have sparked extensive discourse within the medical education community, spurring both excitement and apprehension. Written from the perspective of medical students, this editorial offers insights gleaned through immersive interactions with ChatGPT, contextualized by ongoing research into the imminent role of LLMs in health care. Three distinct positive use cases for ChatGPT were identified: facilitating differential diagnosis brainstorming, providing interactive practice cases, and aiding in multiple-choice question review. These use cases can effectively help students learn foundational medical knowledge during the preclinical curriculum while reinforcing the learning of core Entrustable Professional Activities. Simultaneously, we highlight key limitations of LLMs in medical education, including their insufficient ability to teach the integration of contextual and external information, comprehend sensory and nonverbal cues, cultivate rapport and interpersonal interaction, and align with overarching medical education and patient care goals. Through interacting with LLMs to augment learning during medical school, students can gain an understanding of their strengths and weaknesses. This understanding will be pivotal as we navigate a health care landscape increasingly intertwined with LLMs and artificial intelligence.