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Evidence-backed gain

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…

JAMIA Open · Health

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

Evidence-backed problem

A large language models-assisted and expert-corrected workflow for preoperative anesthesia assessment drafts: A single-centre exploratory feasibility study

Large language models (LLMs) may help organize clinical information, but their use in perioperative settings requires careful evaluation because errors may have immediate safety implications. This study aimed to describe the feasibility and perceived usefulness of a single-centre, expert-corrected LLM workflow for preparing preoperative anesthesia assessment drafts for complex consultation cases. Secondary aims were to describe error patterns identified by anesthesiologists and to explore residents' perceptions…

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Artificial Intelligence-Assisted Chest Radiography: A Prospective Crossover Multi-Reader Study on Diagnostic Performance and Workflow Efficiency

To evaluate the impact of four commercially available AI solutions for chest radiography on diagnostic performance, workflow efficiency, and clinical decision-making in a real-world setting. In this prospective, monocentric, crossover reader study, five readers (one to six years of experience) assessed 1200 consecutive patients undergoing chest radiography (1861 total radiographs) for pulmonary infiltrates, pleural effusions, mediastinal masses, pneumothorax, and pulmonary nodules under five conditions: without…

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Evidence-backed problem

EXPRESS: Relation between Albumin-Corrected Anion Gap and In-Hospital Mortality in Patients with Traumatic Lung Injury: A Multicenter Retrospective Cohort Study and the Development of Machine Learning-Based Prediction Models

Background The anion gap is primarily utilized as an indicator for evaluating acid-base imbalances in critically ill patients. However, its accuracy is reduced in such patients due to low albumin levels. The albumin-corrected anion gap (ACAG) enhances the accuracy of assessing acid-base imbalances. Individuals with traumatic lung injury (TLI) in the intensive care unit (ICU) often have severe metabolic acidosis and hypoalbuminemia. Nevertheless, the association of ACAG with the prognosis of patients with TLI is…

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Diagnostic accuracy of electronic medical record retrieval methods and a large language model for identifying cardiovascular events: a multisite retrospective validation study in a medical system in the United States

To compare the diagnostic accuracy of four available automated electronic medical record (EMR) retrieval methods, including a large language model (LLM)-assisted workflow, against manual chart adjudication for identifying cardiovascular events. Retrospective diagnostic accuracy study. Three sites within a single US tertiary health system. Two adult cohorts with previously adjudicated cardiovascular outcomes were included. Cohort 1 included 2258 patients treated with immune checkpoint inhibitors, and Cohort 2 inc…

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TrialTriage, a Semiautonomous Prescreening Workflow for Resolving Ambiguity in Phase I Oncology Trial Eligibility: Development and Proof-of-Concept Study Using Synthetic Cases

Enrollment in phase I oncology trials remains low largely because potentially eligible patients are not identified and evaluated quickly enough. Current clinical trial matching systems can identify candidate patients from the electronic health record, but cases with missing or uncertain eligibility data are often routed for offline manual review. This delay impedes clarification and prolongs the final eligibility determination. This study evaluated TrialTriage, a semiautonomous system built on the n8n platform a…

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Trustworthy artificial intelligence for rural health care

Regional, rural and remote Australians experience poorer health outcomes and substantially higher rates of suicide and self-harm than those in major cities. Artificial intelligence could support earlier identification of distress, safer triage and more timely care alongside telehealth and clinical decision support, but only if it is treated as a health intervention with explicit safety nets and independent evaluation. We propose a minimum viable governance model, including Indigenous partnership, language safety…

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Mapping the Evolving AI Preferences and Care Needs in Orthopedic Transitional Care From Hospitals to Home: Cross-Sectional Study

Enhanced recovery after surgery protocols have shortened orthopedic hospital stays but have shifted rehabilitation and safety-monitoring tasks to patients and families after discharge. In this study, AI refers to patient-facing digital systems for orthopedic transitional care, including large language model chatbots, computer vision or platform-based monitoring tools, and wearable sensor-enabled systems for education, rehabilitation guidance, motion correction, and risk alerts. However, patient-reported preferen…

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Mapping the Evolving AI Preferences and Care Needs in Orthopedic Transitional Care From Hospitals to Home: Cross-Sectional Study

The Sydney Triage to Admission Risk Tool With Artificial Intelligence (START-AI) to Support Decision Making in Emergency Departments: Model Explainability and Feature Importance Analysis

Objective Evaluate the importance of specific variables contributing to a recently reported Artificial Intelligence (AI) prediction model called Sydney Triage to Admission Risk Tool with Artificial Intelligence (START-AI) to predict inpatient admission from the Emergency Department (ED). Methods A model explainability analysis was undertaken using single-centre ED electronic medical record data over 2 years. The START-AI model, which comprises ensemble machine learning and a transformer-based algorithm to enhanc…

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[Use of artificial intelligence in clinical practice and hospitals]

Artificial intelligence (AI) is increasingly evolving from a research technology into a tool for everyday clinical practice. While early applications primarily focused on medical image analysis, generative AI systems and large language models are now available for a wide range of clinical and administrative tasks. These include medical documentation, literature review, guideline-based knowledge management, patient communication, and workflow optimization. At the same time, diagnostic and therapeutic applications…

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Beyond Thresholds: Can Machine Learning Improve Trauma Field Triage?

BackgroundAccurate triage of trauma patients by Emergency Medical Services (EMS) is essential for optimal outcomes and resource allocation. The 2021 National Field Triage Guidelines (FTG) assist EMS in prehospital triage; however, its collective performance has never been evaluated using a national database. We aimed to evaluate an FTG surrogate and develop a predictive model to identify patients at risk for serious injury.MethodsThe Trauma Quality Improvement Program National Trauma Databank (2017-2020) was que…

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Artificial intelligence in nursing practice: a qualitative study of nurses’ perspectives on opportunities, challenges, and ethical implications

BACKGROUND: The study aims to explore nurses' views on the effects of artificial intelligence (AI) in nursing, focusing on their understanding, practical applications, ethical considerations, and perceived opportunities and threats. METHODS: This qualitative study used semi[Formula: see text]structured interviews to gain comprehensive insights from clinical nurses, adhering to the Standards for Reporting Qualitative Research for methodological rigor. After obtaining ethical approval, researchers conducted semi[F…

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Use of Ambient AI Scribes to Reduce Administrative Burden and Professional Burnout

Importance: While in short supply and high demand, ambulatory care clinicians spend more time on administrative tasks and documentation in the electronic health record than on direct patient care, which has been associated with burnout, intention to leave, and reduced quality of care. Objective: To examine whether ambient AI scribes are associated with reducing clinician administrative burden and burnout. Design, Setting, and Participants: This quality improvement study used preintervention and 30-day postinterv…

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Use of Ambient AI Scribes to Reduce Administrative Burden and Professional Burnout

The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century

As healthcare systems around the world face challenges such as escalating costs, limited access, and growing demand for personalized care, artificial intelligence (AI) is emerging as a key force for transformation. This review is motivated by the urgent need to harness AI's potential to mitigate these issues and aims to critically assess AI's integration in different healthcare domains. We explore how AI empowers clinical decision-making, optimizes hospital operation and management, refines medical image analysi…

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Data-Centric Foundation Models in Computational Healthcare: A Survey

The advent of foundation models (FMs) as an emerging suite of AI techniques has struck a wave of opportunities in computational healthcare. The interactive nature of these models, guided by pre-training data and human instructions, has ignited a data-centric AI paradigm that emphasizes better data characterization, quality, and scale. In healthcare AI, obtaining and processing high-quality clinical data records has been a longstanding challenge, encompassing data quantity, annotation, patient privacy, and ethics…

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