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We aim to use large language models (LLMs) to detect mentions of nuanced psychotherapeutic outcomes and impacts than previously considered in transcripts of interviews with adolescent depression. Our clinical authors previously created a novel coding framework containing fine-grained therapy outcomes beyond the binary classification (eg, depression vs control) based on qualitative analysis embedded within a clinical study of depression. Moreover, we seek to demonstrate that embeddings from LLMs are informative [...]

This study describes the development and deployment of a machine learning (ML) model called Vent.io to predict mechanical ventilation (MV).

The quality of alcohol-related prevention and treatment in US primary care is poor. The purpose of this study was to describe the extent to which Electronic Health Records (EHRs) used by 167 primary care practices across 7 states currently include the necessary prompts, clinical support, and performance reporting essential for improving alcohol-related prevention and treatment in primary care.

Integrating electronic health record (EHR) data with other resources is essential in rare disease research due to low disease prevalence. Such integration is dependent on the alignment of ontologies used for data annotation. The international classification of diseases (ICD) is used to annotate clinical diagnoses, while the human phenotype ontology (HPO) is used to annotate phenotypes. Although these ontologies overlap in the biomedical entities they describe, the extent to which [...]

We sought to characterize indicators of digital literacy among persons testing for COVID-19 and community health workers (CHWs) providing testing via a digital platform in low-income, majority-Latino communities in California.

In 2016, the Department of Veterans Affairs (VA) and the Department of Energy (DOE) established an Interagency Agreement (IAA), the Million Veteran Program-Computational Health Analytics for Medical Precision to Improve Outcomes Now (MVP-CHAMPION) research collaboration.

Research on firearm violence is largely limited to people who experienced acute bodily trauma and death which is readily gathered from Inpatient and Emergency Department settings and mortality data. Exposures to firearm violence, such as witnessing firearm violence or losing a loved one to firearm violence, are not routinely collected in health care. As a result, the true public health burden of firearm violence is underestimated. Clinical notes from electronic [...]

In public health, access to research literature is critical to informing decision-making and to identify knowledge gaps. However, identifying relevant research is not a straightforward task since public health interventions are often complex, can have positive and negative impacts on health inequalities and are applied in diverse and rapidly evolving settings. We developed a "living" database of public health research literature to facilitate access to this information using Natural Language [...]

Many routine patient care items should be reviewed at least daily for intensive care unit (ICU) patients. These items are often incompletely performed, and dynamic clinical decision support tools (CDSTs) may improve attention to these daily items. We sought to evaluate the accuracy of institutionalized electronic health record (EHR) based custom dynamic CDST to support 22 ICU rounding quality metrics across 7 categories (hypoglycemia, venothromboembolism prophylaxis, stress ulcer prophylaxis, mechanical [...]