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 This study aims to explore how health informaticists collaborate with multiple stakeholder groups, each possessing varying levels of comfort and competence with health technology and data. stakeholder engagement is highlighted as a crucial skill for health informaticists, necessitated by the differing competency levels among stakeholders.

 This study seeks to understand pediatric rheumatology patients' and caregivers' utilization and perceptions of immediate access to their electronic health information (EHI) via patient portals.

 Effective data management is critical in medical informatics research, ensuring the success and integrity of clinical studies. However, students often lack comprehensive training in this area, highlighting the need for tailored educational programs.

Early detection of surgical complications allows for timely therapy and proactive risk mitigation. Machine learning (ML) can be leveraged to identify and predict patient risks for postoperative complications. We developed and validated the effectiveness of predicting postoperative complications using a novel surgical Variational Autoencoder (surgVAE) that uncovers intrinsic patterns via cross-task and cross-cohort presentation learning.

 Large language models (LLMs) have shown promise in various professional fields, including medicine and law. However, their performance in highly specialized tasks, such as extracting ICD-10-CM codes from patient notes, remains underexplored.

 To keep pace with the developments in the medical informatics field, the curriculum of the Heidelberg/Heilbronn Medical Informatics Master of Science program is continuously updated. In its latest revision we restructured our master's program to allow more flexibility to accommodate updates and include current topics and to enable students' choices.

We sought to assess whether race, ethnicity, and preferred language were associated with digital health literacy in pediatric caregivers.

 Adolescents are at high risk for sexually transmitted infections (STIs) and frequently present to emergency departments (EDs) for care. Screening for STIs using confidential patient-reported outcomes represents an ideal use of electronic screening methodology.

This study aimed to explore the utilization of a fine-tuned language model to extract expressions related to the Age-Friendly Health Systems 4M Framework (What Matters, Medication, Mentation, and Mobility) from nursing home worker text messages, deploy automated mapping of these expressions to a taxonomy, and explore the created expressions and relationships.