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Background The United States continues to rank as one of the most expensive healthcare systems in the world, and cataract surgery, the most commonly performed surgery, is one of the primary drivers of healthcare expenditure. Increasing efforts have been made to try to minimize U.S.

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Importance: Surgical quality improvement efforts have largely focused on 30-day outcomes, such as readmissions and complications. Surgery may have a sustained impact on the health and quality of life of patients considered frail, yet data are lacking on the long-term health care utilization of patients with frailty following surgery.

Objective: To examine the independent association of preoperative frailty on long-term health care utilization (up to 24 months) following surgery.

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Importance: Access to appropriate postpartum care is essential for improving maternal health outcomes and promoting maternal health equity.

Objective: To analyze the impact of the Nurse-Family Partnership (NFP) home visiting program on use of routine and emergency postpartum care.

Design, Setting, And Participants: This study was a secondary analysis of a randomized clinical trial that enrolled eligible participants between 2016 and 2020 to receive NFP or usual care from a South Carolina Medicaid program.

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Unlabelled: Although evidence-based treatments for Prolonged Grief Disorder (PGD) exist, pretreatment characteristics associated with differential improvement trajectories have not been identified. To identify clinical factors relevant to optimizing PGD treatment outcomes, we used unsupervised and supervised machine learning to study treatment effects from a double-blinded, placebo-controlled, randomized clinical trial. Participants were randomized into four treatment groups for 20 weeks: citalopram with grief-informed clinical management, citalopram with prolonged grief disorder therapy (PGDT), pill placebo with PGDT, or pill placebo with clinical management.

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Hospital quality measures are a vital component of a learning health system, yet they can be costly to report, statistically underpowered, and inconsistent due to poor interrater reliability. Large language models (LLMs) have recently demonstrated impressive performance on health care-related tasks and offer a promising way to provide accurate abstraction of complete charts at scale. To evaluate this approach, we deployed an LLM-based system that ingests Fast Healthcare Interoperability Resources data and outputs a completed Severe Sepsis and Septic Shock Management Bundle (SEP-1) abstraction.

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