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 Predicting 30-day hospital readmissions is crucial for improving patient outcomes, optimizing resource allocation, and achieving financial savings. Existing studies reporting the development of machine learning (ML) models predictive of neurosurgical readmissions do not report factors related to clinical implementation.

 Clinical informatics (CI) has reshaped how medical information is shared, evaluated, and utilized in health care delivery. The widespread integration of electronic health records (EHRs) mandates proficiency among physicians and practitioners, yet medical trainees face a scarcity of opportunities for CI education.

 Recognition of the patient and family's diverse backgrounds and language preference is critical for communicating effectively. In our hospital's electronic health record, a patient or family's identified language for communication is documented in a discrete field known as "preferred language." This field serves as an interdepartmental method to identify patients with a non-English preferred language, creating a bolded banner for non-English speakers easily identifiable by health care professionals. Despite the [...]

 To assess primary care physicians' (PCPs) perception of the need for serious illness conversations (SIC) or other palliative care interventions in patients flagged by a machine learning tool for high 1-year mortality risk.

 To understand the status quo and related influencing factors of machine alarm fatigue of hemodialysis nurses in tertiary hospitals in Liaoning Province.

 Managing acute postoperative pain and minimizing chronic opioid use are crucial for patient recovery and long-term well-being.

 Patient data are fragmented across multiple repositories, yielding suboptimal and costly care. Record linkage algorithms are widely accepted solutions for improving completeness of patient records. However, studies often fail to fully describe their linkage techniques. Further, while many frameworks evaluate record linkage methods, few focus on producing gold standard datasets. This highlights a need to assess these frameworks and their real-world performance. We use real-world datasets and expand upon previous [...]

 Falls in older adults are a serious public health problem that can lead to reduced quality of life or death. Patients often do not receive fall prevention guidance from primary care providers (PCPs), despite evidence that falls can be prevented. Mobile health technologies may help to address this disparity and promote evidence-based fall prevention.

 Provider burnout due to workload is a significant concern in primary care settings. Workload for primary care providers encompasses both scheduled visit care and non-visit care interactions. These interactions are highly influenced by patients' health conditions or acuity, which can be measured by the Adjusted Clinical Group (ACG) score. However, new patients typically have minimal health information beyond social determinants of health (SDOH) to determine ACG score.