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To solve major clinical natural language processing (NLP) tasks using a unified text-to-text learning architecture based on a generative large language model (LLM) via prompt tuning.

Phenotyping is a core task in observational health research utilizing electronic health records (EHRs). Developing an accurate algorithm demands substantial input from domain experts, involving extensive literature review and evidence synthesis. This burdensome process limits scalability and delays knowledge discovery. We investigate the potential for leveraging large language models (LLMs) to enhance the efficiency of EHR phenotyping by generating high-quality algorithm drafts.

Large language models (LLMs) are poised to change care delivery, but their impact on health equity is unclear. While marginalized populations have been historically excluded from early technology developments, LLMs present an opportunity to change our approach to developing, evaluating, and implementing new technologies. In this perspective, we describe the role of LLMs in supporting health equity.

Our article demonstrates the effectiveness of using a validated framework to create a ChatGPT prompt that generates valid nursing care plan suggestions for one hypothetical older patient with lung cancer.

In this study, we investigate the potential of large language models (LLMs) to complement biomedical knowledge graphs in the training of semantic models for the biomedical and clinical domains.

 Heart failure is a complex clinical syndrome noted on approximately one in eight death certificates in the United States. Vital to reducing complications of heart failure and preventing hospital readmissions is adherence to heart failure self-care routines. Mobile health offers promising opportunities for enhancing self-care behaviors by facilitating tracking and timely reminders.

 The overall goal of this work is to create a patient-reported outcome (PRO) and decision support system to help postpartum patients determine when to seek care for concerning symptoms. In this case study, we assessed differences in perspectives for application design needs based on race, ethnicity, and preferred language.

 Though public health is an information-intense profession, there is a paucity of workforce with Public Health Informatics and Technology (PHIT) skills, which was evident during the coronavirus disease 2019 (COVID-19) pandemic. This need is addressed through the PHIT workforce program (2021-2025) by the Office of the National Coordinator for training and to increase racial and ethnic diversity in the PHIT workforce. The objective is to share details on the Training [...]

 Over the past 30 years, the American Medical Informatics Association (AMIA) has played a pivotal role in fostering a collaborative community for professionals in biomedical and health informatics. As an interdisciplinary association, AMIA brings together individuals with clinical, research, and computer expertise and emphasizes the use of data to enhance biomedical research and clinical work. The need for a recognition program within AMIA, acknowledging applied informatics skills by members, led [...]

 The coronavirus disease 2019 pandemic accelerated the use of telehealth. However, this also exacerbated health care disparities for vulnerable populations.