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Interruptive clinical decision support (CDS) alerts are intended to standardize patient care and prevent harm. However, failures can occur even in organizations with mature CDS governance and advanced analytics. These breakdowns, marked by excessive firings, workflow disruption, and clinician dissatisfaction, can provide insights into systemic weaknesses in CDS design, testing, and monitoring processes.This study aimed to examine a CDS alert malfunction as a lens for identifying system-level gaps and propose [...]

Rule-based structured data algorithms and natural language processing (NLP) approaches applied to unstructured clinical notes have limited accuracy and poor generalizability for identifying immunosuppression. Large language models (LLMs) may effectively identify patients with heterogenous types of immunosuppression from unstructured clinical notes. We compared the performance of LLMs applied to unstructured notes for identifying patients with immunosuppressive conditions or immunosuppressive medication use against 2 baselines: (1) structured data algorithms using diagnosis [...]

Building upon our previous work on predicting treatment retention in medications for opioid use disorder, we aimed to improve 6-month retention prediction in buprenorphine-naloxone (BUP-NAL) therapy by incorporating features derived from large language models (LLMs) applied to unstructured clinical notes.

Text embeddings are promising for semantic tasks, such as retrieval augmented generation (RAG). However, their application in health care is underexplored due to a lack of benchmarking methods. We introduce a scalable benchmarking method to test embeddings for health-care semantic tasks.

The objective of this study is to evaluate retrieval-augmented prediction for forecasting hospital length of stay (LOS) following surgery compared to traditional machine learning (ML), standalone large language models (LLMs), and retrieval-augmented generation (RAG) approaches.

Externalizing behaviors in children, such as aggression, hyperactivity, and defiance, are influenced by complex interplays between genetic predispositions and environmental factors, particularly parental behaviors. Unraveling these intricate causal relationships can benefit from the use of robust data-driven methods.

To create a theoretical framework of mental health risk factors to inform the development of prediction models for young people's mental health problems.

Electronic health record (EHR) work may differently affect women and men physicians. Identifying gender discrepancies in EHR work across different specialties may inform strategies to reduce EHR burdens.

[This corrects the article DOI: 10.1093/jamiaopen/ooz061.].