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Temporal drift, defined as changes over time in underlying data distributions, can degrade the performance of clinical prediction models. In head and neck cancer (HNC) radiotherapy, evolving proton and carbon ion therapies may shift the risk of oral mucositis over time. This study aimed to compare machine learning (ML) strategies for mitigating temporal drift in predicting grade ≥2 oral mucositis among patients treated with particle therapy.

This study aimed to evaluate how differences in case complexity affect information-processing burden, as measured by eye-tracking, as well as the efficiency of electronic health record (EHR) use among healthcare providers in the intensive care unit.

Hybrid in-person and telehealth work environments are now common among health care providers. When in-person and telehealth services are not well integrated, provider workload could increase, negatively affecting provider satisfaction and burnout and hindering implementation of interventions aimed at improving quality. A lack of measures of telehealth integration has hindered studies of such impacts. This article presents the Integration of Telehealth and In-Person Services (ITIPS) survey, developed to assess telehealth [...]

Understand the qualitative impact of an ambient artificial intelligence (AI) documentation platform on clinicians' experiences and workflows.

Incomplete or incorrect causal theories are a key source of bias in machine learning (ML) algorithms. Community-engaged methodologies provide an avenue for mitigating this bias through incorporating causal insights from community stakeholders into ML development. In health applications, community-engaged approaches can enable the study of social drivers of health (SDOH), which are known to shape health inequities. However, it remains challenging for SDOH to inform ML algorithms, partially because SDOH [...]

Accurate triage in emergency departments (ED) is critical for appropriate resource allocation. While artificial intelligence (AI) has been explored for triage, prior models relied on summarized clinical scenarios. We aimed to develop and evaluate large language models (LLMs) trained on real-world clinical conversations to classify patient urgency.

To explore the complexities of eliminating race correction in clinical artificial intelligence (AI), the pitfalls of naive solutions, and to propose systematic strategies for equitable model development.

To evaluate the performance of a locally deployed adaptation of TrialGPT, a large language model (LLM) system for identifying trial-eligible patients from unstructured electronic health record (EHR) data.

This study explores the use of advanced natural language processing (NLP) techniques to enhance food classification and dietary analysis using raw text input from a diet tracking app.