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Artificial intelligence (AI) scribes may reduce the documentation burden and improve clinician experience through generative AI automatically producing provider note sections from recordings of patient-provider encounters.We aimed to examine the impact of AI scribes on clinician experience, clinician efficiency, and business efficiency measures among pediatric subspecialty physicians.We randomized pediatric subspecialty providers with ≥0.5 clinical full-time equivalent and stable electronic health record (EHR) log metrics to use Microsoft/Nuance Digital Ambient eXperience [...]

To develop an image retrieval pipeline capable of identifying specific series of thoracic aortic computed tomography (CT) scans from a diverse database.

To characterize patient and clinician perceived barriers and facilitators to using electronic patient-generated data (PGD) in safety-net systems.

To conduct a meta-ethnographic synthesis summarizing the overarching themes of the qualitative literature on nurse interaction with medication administration technologies (MAT) comprising electronic medication administration record (eMAR) and bar-coded medication administration (BCMA).

Immunotherapies have revolutionized the landscape of cancer treatments. However, our understanding of response patterns in advanced cancers treated with immunotherapy remains limited. By leveraging routinely collected noninvasive longitudinal and multimodal data with artificial intelligence, we could unlock the potential to transform immunotherapy for cancer patients, paving the way for personalized treatment approaches.

Diagnosing post-traumatic stress disorder (PTSD) remains a challenge due to symptom variability and comorbidities. Linguistic analysis offers an innovative approach to identify PTSD symptoms and severity. This systematic review aimed at identifying linguistic features associated with PTSD, assessing the quality and limitations of existing studies, summarizing the predictive performance of identified models, and describing the clinical utility of these models.

Electronic health record (EHR) patient portal messaging has become an essential tool for patient-clinician communication by improving accessibility to primary care. While messaging is beneficial for patients, it can increase clinicians' workloads. Female clinicians receive a greater number of EHR messaging, resulting in an increased workload.This evaluation explores the factors in clinician gender disparity in EHR messaging burden.The first phase of the evaluation included a retrospective analysis of the messages [...]

In dentistry, social determinants of health (SDoH) are potentially recorded in the clinical notes of electronic dental records. The objective of this study was to examine the availability of SDoH data in dental clinical notes and evaluate natural language processing methods to extract SDoH from dental clinical notes.A set of 1,000 dental clinical notes was sampled from a dataset of 105,311 patient visits to a dental clinic and manually annotated [...]

Accurate discharge summaries are essential for effective communication between hospital and outpatient providers but generating them is labor-intensive. Large language models (LLMs), such as GPT-4, have shown promise in automating this process, potentially reducing clinician workload and improving documentation quality. A recent study using GPT-4 to generate discharge summaries via concatenated clinical notes found that while the summaries were concise and coherent, they often lacked comprehensiveness and contained errors. To [...]