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Search Results for Machine-learning
Abstract Number: 0026
IMPACT OF A MACHINE LEARNING-DRIVEN SMARTALERT ON REPETITIVE INPATIENT LAB ORDERING
SHM Converge 2025
Background: An estimated 20-30% of inpatient standing labs are medically unnecessary1, contributing to iatrogenic anemia2, sleep disruption3, and increased healthcare costs. The Society of Hospital Medicine identified routine repetitive complete blood count (CBC) and chemistry (BMP) as a common wasteful inpatient practice4. At our institution, repetitive inpatient CBC, BMP, magnesium, and phosphorus account for 500,000 [...]
Abstract Number: 0114
THE EFFECT OF SEPSIS ALERT SYSTEMS ON MORTALITY: A SYSTEMATIC REVIEW AND META ANALYSIS
SHM Converge 2025
Background: The Surviving Sepsis Campaign strongly recommends that all hospitals screen for sepsis as part of performance improvement. The effect of screening for sepsis on mortality, length of stay, and time to antibiotics is uncertain. Methods: A systematic literature search was conducted using Cochrane Library, Google Scholar, Ovid Embase, Ovid Medline, Scopus, and Web of [...]
Abstract Number: 0116
MACHINE LEARNING-BASED PREDICTION OF HYPERGLYCEMIA IN HOSPITALIZED PATIENTS WITH DIABETES: AN EIGHT-YEAR STUDY OF EHR RECORDS ACROSS 19 HOSPITAL SITES
SHM Converge 2025
Background: Individuals with diabetes have a 2–3 fold higher hospitalization rate compared to those without diabetes. During hospitalization, individuals with diabetes frequently experience elevated blood glucose levels (or, hyperglycemia) (1), which can increase monitoring by nurses and hospitalists, length of stay, and healthcare cost. Therefore, we sought to develop a machine learning model to predict [...]
Abstract Number: 0126
PREDICTING POST – RETURN OF SPONTANEOUS CIRCULATION OUTCOMES: A MACHINE LEARNING APPROACH TO SEDATION ANALYSIS
SHM Converge 2025
Background: In-hospital cardiac arrest (IHCA) remains a leading cause of mortality in the United States, with an estimated 290,000 cases annually and survival to discharge rates ranging from 15% to 40% as of 2024. For those patients who achieve return of spontaneous circulation (ROSC), predicting their neurological outcome and survival in the intensive care unit [...]
Abstract Number: 0164
PREDICTING HYPOGLYCEMIA USING MACHINE LEARNING IN HOSPITALIZED PATIENTS WITH DIABETES: A LARGE-SCALE STUDY ACROSS 19 HOSPITALS (2017-2024)
SHM Converge 2025
Background: Hypoglycemia occurs frequently in hospitalized adults with diabetes, and is associated with adverse clinical events, increased use of rapid response teams, prolonged hospital length of stay, and higher healthcare costs (1). Identifying risk of hypoglycemia in hospitalized adults is vital to preventing adverse events and maximizing patient safety. However, there are no tools/models to [...]
Abstract Number: 0270
ACCURACY OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN CARDIOVASCULAR DISEASES: A SYSTEMATIC REVIEW
SHM Converge 2025
Background: According to the Global Burden of Disease (GBD) study, 523 million (95% UI: 497 to 550 million) people worldwide had cardiovascular diseases (CVDs) in 2019, resulting in a significant increase in DALYs and years of life lost, mostly due to ischemic heart disease. There are various accurate and economical gold standard tests to identify [...]
Abstract Number: 0286
PATIENT INTERACTION PHENOTYPES WITH A POST-DISCHARGE TEXT MESSAGING SERVICE AND THEIR ASSOCIATION WITH HOSPITAL REVISITS
SHM Converge 2025
Background: Automated bidirectional text messaging has emerged as a compelling strategy to facilitate communication between patients and the health system after hospital discharge. Understanding the unique ways in which patients interact with these messaging programs can inform future efforts to tailor their design to individual patient styles and needs. The aim of this study was [...]
Abstract Number: 0303
LEARNING BEHAVIORS OF HOSPITAL MEDICINE RESIDENT PHYSICIANS: INSIGHTS FROM EHR AUDIT LOGS
SHM Converge 2025
Background: Resident physicians are responsible for mastering a vast array of clinical and affective competencies. In the modern clinical learning environment, it is vital that residents can effectively use the electronic health record (EHR) to deliver safe and high-quality care, and that attending physicians and health organizations develop standards by which to supervise trainees in [...]
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