[Reducing the environmental impact of medication; the role of the prescriber]. / Vermindering van de milieu-impact van medicatie.

Medication represents an aspect of healthcare with significant opportunity for reduction of environmental impact. Prescribing practitioners play an important role in mitigating this impact through various interventions. This includes minimizing unnecessary medication usage and waste, as well as providing concrete recommendations to diminish the direct and indirect environmental effects of prescribed medications. Unfortunately, the current lack of comprehensive information hinders the selection of the treatment alternatives with the lowest environmental impact. Therefore, further research and the promotion of transparency are essential to make such informed choices feasible in the future.

Using artificial intelligence to create diverse and inclusive medical case vignettes for education.

AIMS: Medical case vignettes play a crucial role in medical education, yet they often fail to authentically represent diverse patients. Moreover, these vignettes tend to oversimplify the complex relationship between patient characteristics and medical conditions, leading to biased and potentially harmful perspectives among students. Displaying aspects of patient diversity, such as ethnicity, in written cases proves challenging. Additionally, creating these cases places a significant burden on teachers in terms of labour and time. Our objective is to explore the potential of artificial intelligence (AI)-assisted computer-generated clinical cases to expedite case creation and enhance diversity, along with AI-generated patient photographs for more lifelike portrayal. METHODS: In this study, we employed ChatGPT (OpenAI, GPT 3.5) to develop diverse and inclusive medical case vignettes. We evaluated various approaches and identified a set of eight consecutive prompts that can be readily customized to accommodate local contexts and specific assignments. To enhance visual representation, we utilized Adobe Firefly beta for image generation. RESULTS: Using the described prompts, we consistently generated cases for various assignments, producing sets of 30 cases at a time. We ensured the inclusion of mandatory checks and formatting, completing the process within approximately 60 min per set. CONCLUSIONS: Our approach significantly accelerated case creation and improved diversity, although prioritizing maximum diversity compromised representativeness to some extent. While the optimized prompts are easily reusable, the process itself demands computer skills not all educators possess. To address this, we aim to share all created patients as open educational resources, empowering educators to create cases independently.