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 Health informatics education is pivotal in integrating diversity, equity, inclusion, and accessibility (DEIA) principles into curricula and leveraging data with equity considerations. Integrating clinically driven data with other datasets is crucial to comprehensive understanding of patient care demographics, experiences, and outcomes to create equity-minded data storytelling. Publicly available Healthy People 2030 (HP2030) resources complement academic electronic health records, supporting tailored learning activities in informatics education to enhance educational utility through [...]

Reinforcement learning (RL) represents a pivotal avenue within natural language processing (NLP), offering a potent mechanism for acquiring optimal strategies in task completion. This literature review studies various NLP applications where RL has demonstrated efficacy, with notable applications in healthcare settings.

To address the challenges of sharing clinical research data through the implementation of cloud-based virtual desktops, enhancing collaboration among researchers while maintaining data security.

 Choice architecture refers to the design of decision environments, which can influence decision-making in health care. Nudges are subtle adjustments in these environments that guide decisions toward desired outcomes. For example, computerized provider order entry within electronic health records (EHRs) recommends frequencies for interventions such as nursing assessments and medication administrations, but these can link to around-the-clock schedules without clinical necessity.

 High-value care aims to enhance meaningful patient outcomes while reducing costs and is accelerated by curating data across health care systems through common data models (CDMs), such as Observational Medical Outcomes Partnership (OMOP). Meaningful patient outcomes, such as physical function, must be included in these CDMs. However, the extent to which physical therapy assessments are covered in the OMOP CDM is unclear.

Large language models (LLMs) can assist providers in drafting responses to patient inquiries. We examined a prompt engineering strategy to draft responses for providers in the electronic health record. The aim was to evaluate the change in usability after prompt engineering.

 Social media networks have been found to provide emotional, instrumental, and social support, which may contribute to improved adherence to postbariatric surgery care recommendations.

Adverse drug reactions (ADRs) are a significant healthcare concern. They are often documented as free text in electronic health records (EHRs), making them challenging to use in clinical decision support systems (CDSS). The study aimed to develop a text mining algorithm to identify ADRs in free text of Dutch EHRs.

Hospital at Home (HaH) programs currently lack decision support tools to help efficiently navigate the complex decision-making process surrounding HaH as a care option. We assessed user needs and perspectives to guide early prototyping and co-creation of 4PACS (Partnering Patients and Providers for Personalized Acute Care Selection), a decision support app to help patients make an informed decision when presented with discrete hospitalization options.

Clinical note section identification helps locate relevant information and could be beneficial for downstream tasks such as named entity recognition. However, the traditional supervised methods suffer from transferability issues. This study proposes a new framework for using large language models (LLMs) for section identification to overcome the limitations.