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.

Experiences and perspectives of older patients with a return visit to the emergency department within 30 days: patient journey mapping.

PURPOSE: Up to 22% of older patients who visit the emergency department (ED) have a return visit within 30 days. To achieve patient-centered care for this group at the ED it is important to involve the patient perspective and strive to provide the best possible experience. The aim of this study was to gain insight into the experiences and perspectives of older patients from initial to return ED visit by mapping their patient journey. METHODS: We performed a qualitative patient journey study with 13 patients of 70 years and older with a return ED visit within 30 days who presented at the Amsterdam UMC, a Dutch academic hospital. We used semi-structured interviews focusing on the patient experience during their journey and developed a conceptual framework for coding. RESULTS: Our sample consisted of 13 older patients with an average age of 80 years, and 62% of them were males. The framework contained a timeline of the patient journey with five chronological main themes, complemented with an 'experience' theme, these were divided into 34 subthemes. Health status, social system, contact with the general practitioner, aftercare, discharge and expectations were the five main themes. The experiences regarding these themes differed greatly between patients. The two most prominent subthemes were waiting time and discharge communication, which were mostly related to a negative experience. CONCLUSIONS: This study provides insight into the experiences and perspectives of older patients from initial to return ED visit. The two major findings were that lack of clarity regarding waiting times and suboptimal discharge communication contributed to negative experiences. Recommendations regarding waiting time (i.e. a two-hour time out at the ED), and discharge communication (i.e. checklist for discharge) could contribute to a positive ED experience and thereby potentially improve patient-centered care.