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Hospitals & Care · 38 stories · page 1 of 3

Using interpretable machine learning models to predict the occurrence of severe influenza in hospitalized children: a retrospective cohort study
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Using interpretable machine learning models to predict the occurrence of severe influenza in hospitalized children: a retrospective cohort study

Influenza, a prevalent disease, significantly threatens public health. Accurately predicting severe influenza occurrences is crucial for developing personalized prevention strategies and treatment plans. This study aimed to construct a highly interpretable model to assess the risk of severe influenza in hospitalized children, using the SHapley Additive exPlanation (SHAP) method to interpret the Random Forest (RF) model and identify risk factors for severe influenza. A retrospective cohort study was conducted, co…

BMC Medical Informatics and Decision Making
Artificial Intelligence Triage of Urgent Versus Non-Urgent CT Brain Findings to Support Expedited Emergency Department Disposition: A Retrospective Validation Study
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Artificial Intelligence Triage of Urgent Versus Non-Urgent CT Brain Findings to Support Expedited Emergency Department Disposition: A Retrospective Validation Study

Objective Evaluate the diagnostic accuracy of an artificial intelligence (AI) model for identifying urgent computed tomography brain (CTB) findings in consecutive emergency department (ED) patients and estimate the proportion eligible for expedited disposition. Methods We retrospectively analysed 3424 consecutive non-contrast CTB scans from adults presenting to a quaternary ED in 2024. The AI model (Harrison Enterprise CTB) classified each scan as "urgent" or "non-urgent." The reference standard was the index co…

A Machine Learning-Derived Risk Scorecard for Pneumonia Hospitalization in Japanese Old-Old Adults
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A Machine Learning-Derived Risk Scorecard for Pneumonia Hospitalization in Japanese Old-Old Adults

Aim To develop and internally validate a machine learning-based risk scorecard for 1-year pneumonia hospitalization among community-dwelling Japanese adults aged ≥ 75 years using routinely collected frailty screening and claims data. Methods We conducted a retrospective cohort study of 1 098 404 community-dwelling adults aged ≥ 75 years who completed the Questionnaire for Medical Checkup of Old-Old (QMCOO) between April 2020 and March 2024, using the Late-Stage Medical Care System claims database. Data were spli…

Artificial intelligence and perioperative nurses in error detection during operating room transfers: A simulation-based comparison
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Artificial intelligence and perioperative nurses in error detection during operating room transfers: A simulation-based comparison

BackgroundThe safe and efficient transfer of patients to the operating room is a critical component of surgical care. The growing integration of artificial intelligence (AI) in healthcare introduces new possibilities for enhancing clinical decision-making.ObjectiveThis study compared the performance of AI and perioperative nurses with varying experience levels in identifying errors during the preoperative patient transfer process.MethodsA controlled simulation study was conducted involving three nurses (novice,…

Hospital Artificial Intelligence Tools and Inpatient Utilization and Costs in Older Adults with Alzheimer's Disease and Related Dementias
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Hospital Artificial Intelligence Tools and Inpatient Utilization and Costs in Older Adults with Alzheimer's Disease and Related Dementias

Background Hospitals are increasingly adopting artificial intelligence and machine learning (AI/ML) tools to support clinical decision making and care management. However, evidence on how hospital AI/ML adoption relates to inpatient utilization and spending among clinically complex populations remains limited. Older adults with Alzheimer's disease and related dementias (ADRD) experience higher rates of readmissions and potentially avoidable hospitalizations. Methods Cross-sectional study was conducted using 2023…

Health

Temporal and cross-site validation of an AI system for self-harm detection

Adequate self-harm surveillance is a key part of suicide prevention. Our previous research demonstrated that an artificial intelligence (AI)-based system could effectively detect self-harm in emergency department triage notes. However, the system was developed using data from a single hospital, raising concerns about its generalisability. Here, we aim to validate the system prospectively and externally to better understand its portability across hospitals. We leveraged emergency department data from two Australi…

Temporal and cross-site validation of an AI system for self-harm detection
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A novel use of AI for prediction of clinical deterioration in a post-acute hospital

There is a growing literature on the prediction of risk of deterioration in hospital settings, including by leveraging artificial intelligence (AI) models. However, this literature has focused on acute-care hospitals, rather than post-acute facilities, where the risk of deterioration remains high. Post-acute facilities tend to have lower digital maturity and poorer data foundations, as well as less rich physiologic data, making the implementation of AI tools for deterioration challenging. In this study, we demon…

A novel use of AI for prediction of clinical deterioration in a post-acute hospital
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A Real-World Evaluation of Large Language Model-Generated Hospital Courses in Pediatrics

Background Large language model (LLM)-generated hospital courses are increasingly integrated into electronic health records (EHRs), yet their accuracy and safety in pediatric populations remain poorly characterized. Objective To evaluate the accuracy, text quality, and perceived potential harm of EHR-integrated and LLM-generated hospital courses in pediatric inpatient care during early clinical implementation. Methods We conducted a descriptive evaluation from June 10 to August 8, 2025, at an academic freestandi…

A Real-World Evaluation of Large Language Model-Generated Hospital Courses in Pediatrics
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Evaluating the Accuracy, Empathy, and Readability of Generative AI Versus Registered Nurses in Discharge Planning: A Vignette-Based Study

Aim To compare the multidimensional performance of discharge instructions generated by generative AI (GPT-4) versus those created by clinical registered nurses across three dimensions-accuracy, empathy and readability-and to explore the impact of patient. Design A prospective, double-blind, vignette-based cross-sectional study. Methods Five standardized multidisciplinary discharge scenarios were constructed. Discharge instructions were generated independently by five registered nurses and GPT-4. Fifteen clinical…

Evaluating the Accuracy, Empathy, and Readability of Generative AI Versus Registered Nurses in Discharge Planning: A Vignette-Based Study
Health

An Interpretable Machine Learning Framework with Clinical Nomogram for Predicting In-Hospital Mortality in Acute Ischemic Stroke Using High-Granularity Bedside Data

This multicenter study developed and validated an interpretable machine learning model integrating granular nursing and emergency department data collected within the first 24 hours to predict in-hospital mortality in acute ischemic stroke (AIS). We analyzed a retrospective cohort of 5,014 adult AIS patients from three tertiary academic centers (2019-2023). Centers A and B (n=3,512) formed the development cohort; Center C (n=1,502) served as the external validation cohort. Sixty-three predictors across seven dom…

An Interpretable Machine Learning Framework with Clinical Nomogram for Predicting In-Hospital Mortality in Acute Ischemic Stroke Using High-Granularity Bedside Data
Health

Reinventing the echocardiography workflow: from manual quantification to artificial intelligence-driven comprehensive interpretation

Echocardiography remains the cornerstone of cardiovascular imaging. However, traditional workflows including manual acquisition, sequential measurement, and expert interpretation face challenges from increased clinical demand, workforce shortage, and the physical burden of repetitive scanning. Artificial intelligence (AI) has begun to address these issues, transitioning from proof-of-concept to prospective clinical evaluations. Recent evidence suggests that AI integration reduces examination time and automates m…

Reinventing the echocardiography workflow: from manual quantification to artificial intelligence-driven comprehensive interpretation
Health

Adaptation of a fair individualized polysocial risk score for hospitalization risk prediction

Objectives We adapted the individualized polysocial risk score (iPsRS), a machine learning model originally developed for patients with type 2 diabetes, to evaluate its generalizability in predicting 1-year hospitalization risk in a disease-agnostic adult cohort, with attention to fairness and explainability. Materials and methods The study utilized de-identified electronic health record data from a retrospective cohort of 17 857 adult patients at the University of Florida Health. The original iPsRS framework wa…

Adaptation of a fair individualized polysocial risk score for hospitalization risk prediction