Publications by authors named "Mor Saban"
Background And Objective: Medication errors in pediatric care remain a significant healthcare challenge despite technological advancements, necessitating innovative approaches. This study aims to evaluate Large Language Models' (LLMs) potential in reducing pediatric medication dosage calculation errors compared to experienced nurses.
Methods: This cross-sectional study (June-August 2024) involved 101 nurses from pediatric and neonatal departments and three LLMs (ChatGPT-4o, Claude-3.
Background: Patient-reported outcomes, such as satisfaction with care, are essential for assessing and improving healthcare quality, especially in populations with chronic conditions like hemodialysis patients. In diverse societies, understanding how ethnic background influences patient satisfaction and clinical outcomes is crucial for addressing health disparities. However, the relationship between ethnic background, patient satisfaction, and clinical outcomes has not been thoroughly investigated in Israeli hemodialysis patients.
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- A study evaluated how accurately the language model GPT-4 can assign Emergency Severity Index (ESI) scores compared to senior nurses and doctors in an emergency department.
- GPT-4 assigned lower severity scores than human evaluators (median 2.0 vs. 3.0), suggesting it may underestimate patient severity.
- While GPT-4 shows promise for improving triage processes, its limitations indicate the need for further refinement before widespread use in clinical settings.
Background: Individuals with dementia are particularly vulnerable during emergency situations due to challenges with cognition, mobility, and daily functioning. However, little is known about how disruptive events may specifically impact the health of those with dementia.
Objective: To evaluate changes in health outcomes for individuals with and without dementia surrounding the Israel-Gaza war in October 2023.
Introduction: Individuals with chronic kidney disease (CKD) are at increased risk of thrombotic events and bleeding. Acetylsalicylic acid (ASA), an effective antiplatelet agent, is one of the most frequently used medications for both primary and secondary prevention of cardiovascular disease (CVD). However, it can also contribute to bleeding events due to its inherent antiplatelet effect.
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- Exposure to the Israel-Hamas conflict negatively affected the health of women, especially those in hostage families, who reported the worst impacts on both physical and mental health compared to crisis volunteers and the general population.
- A cross-sectional survey of 318 Hebrew-speaking women revealed increased rates of poor physical health, mental health issues, and unhealthy lifestyle choices during the conflict, with hostage families showing the highest levels of distress.
- The findings highlight the urgent need for mental health support, particularly for hostage families, as they reported significant deterioration in well-being and a high demand for assistance during this challenging period.
The gold standard to estimate muscle mass and quality is computed tomography (CT) scan. Lower mass and density (intramuscular fat infiltration) of skeletal muscles are markers of sarcopenia, associated with increased mortality risk, impaired physical function, and poorer prognosis across various populations and medical conditions. We aimed to describe standard reference values in healthy population, prospective kidney donors, and correlate clinical parameters to muscle mass and density.
View Article and Find Full Text PDFBackground: Ensuring appropriate computed tomography (CT) utilization optimizes patient care while minimizing radiation exposure. Decision support tools show promise for standardizing appropriateness.
Objectives: In the current study, we aimed to assess CT appropriateness rates using the European Society of Radiology (ESR) iGuide criteria across seven European countries.
Article Synopsis
- The COVID-19 pandemic had a notable impact on emergency department operations, affecting nursing processes, triage accuracy, and patient wait times, which are crucial for enhancing patient outcomes.
- This study compared data from 224 electronic medical records, focusing on differences in triage accuracy and wait times before and during the pandemic across similar sociodemographic and clinical profiles.
- Findings showed high triage accuracy, minor changes in nursing wait times, decreased physician wait times, and improved documentation during the pandemic, highlighting areas for future research and better pandemic preparedness strategies.
Background: Mild traumatic brain injuries (mTBIs) pose a significant risk, particularly in the elderly population on anticoagulation therapy. The safety of discharging these patients from the emergency department (ED) with a negative initial computed tomography (CT) scan has been debated due to the risk of delayed intracranial hemorrhage (d-ICH).
Objective: To compare outcomes, including d-ICH, between elderly patients on anticoagulation therapy presenting with mTBI who were admitted versus discharged from the ED after an initial negative head CT scan.
Background: As generative artificial intelligence (GenAI) tools continue advancing, rigorous evaluations are needed to understand their capabilities relative to experienced clinicians and nurses. The aim of this study was to objectively compare the diagnostic accuracy and response formats of ICU nurses versus various GenAI models, with a qualitative interpretation of the quantitative results.
Methods: This formative study utilized four written clinical scenarios representative of real ICU patient cases to simulate diagnostic challenges.
Background: Providing emergency care during conflict poses unique challenges for frontline hospitals. Barzilai Medical Center (BUMCA) in Ashkelon, Israel is a Level I trauma center located close to the Gaza border. During the November 2023 escalation of conflict, BUMCA experienced surging numbers of civilian and military trauma patients while also coming under rocket fire.
View Article and Find Full Text PDFRadiology referral quality impacts patient care, yet factors influencing quality are poorly understood. This study assessed the quality of computed tomography (CT) referrals, identified associated characteristics, and evaluated the ESR-iGuide clinical decision support tool's ability to optimize referrals. A retrospective review analyzed 300 consecutive CT referrals from an acute care hospital.
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- - This study found that patient satisfaction and quality of life during dialysis treatment varied significantly among three different centers, indicating that local factors might affect these outcomes.
- - Although one center with better dialysis measures reported lower patient satisfaction, the relationship between satisfaction and quality of care remains complex and requires further research.
- - The findings suggest that patient demographics and experiences significantly influence satisfaction, which does not always align with clinical quality metrics like Kt/V, highlighting the need for a more comprehensive understanding of patient-centered care.
Individuals with dementia face increased vulnerability during crises like armed conflicts. However, little is known about how conflicts affect dementia care delivery and patients' health. We conducted a longitudinal cohort study using medical record data.
View Article and Find Full Text PDFObjective: This study compared COVID-19 outcomes between vaccinated and unvaccinated older adults with and without cognitive impairment.
Method: Electronic health records from Israel from March 2020-February 2022 were analyzed for a large cohort (N = 85,288) aged 65 + . Machine learning constructed models to predict mortality risk from patient factors.
Background: As populations age globally, effectively managing geriatric health poses challenges for primary care. Comprehensive geriatric assessments (CGAs) aim to address these challenges through multidisciplinary screening and coordinated care planning. However, most CGA tools and workflows have not been optimised for routine primary care delivery.
View Article and Find Full Text PDFAim: To assess the clinical reasoning capabilities of two large language models, ChatGPT-4 and Claude-2.0, compared to those of neonatal nurses during neonatal care scenarios.
Design: A cross-sectional study with a comparative evaluation using a survey instrument that included six neonatal intensive care unit clinical scenarios.
Article Synopsis
- Frontline hospitals in conflict zones face significant challenges in providing emergency care due to threats to personnel and infrastructure; existing research often overlooks the practical aspects of operating under fire.
- This study focuses on Barzilai University Medical Center in Israel during heightened conflict, analyzing 232 patient cases to understand triage methods, surge capacity, and continuity of care amidst hazardous conditions.
- Findings reveal a direct correlation between missile attacks and patient surges, highlighting the need for improved trauma response strategies in frontline hospitals to enhance emergency care delivery during conflict.
Background: Conflict profoundly impacts community health and well-being. While post-conflict research exists, little is known about initial effects during active hostilities.
Objective: To assess self-reported changes in health behaviors, distress, and care access within one month of regional warfare onset in a conflict-affected community.
Background: Chronic wounds present significant challenges for patients and nursing care teams worldwide. Digital health tools offer potential for more standardised and efficient nursing care pathways but require further rigorous evaluation.
Objective: This retrospective matched cohort study aimed to compare the impacts of a digital tracking application for wound documentation versus traditional manual nursing assessments.
Aim: This study explores the potential of a generative artificial intelligence tool (ChatGPT) as clinical support for nurses. Specifically, we aim to assess whether ChatGPT can demonstrate clinical decision-making equivalent to that of expert nurses and novice nursing students. This will be evaluated by comparing ChatGPT responses to clinical scenarios to those of nurses on different levels of experience.
View Article and Find Full Text PDFBackground: Studies have demonstrated that 50% to 80% of patients do not receive an International Classification of Diseases (ICD) code assigned to their medical encounter or condition. For these patients, their clinical information is mostly recorded as unstructured free-text narrative data in the medical record without standardized coding or extraction of structured data elements. Leumit Health Services (LHS) in collaboration with the Israeli Ministry of Health (MoH) conducted this study using electronic medical records (EMRs) to systematically extract meaningful clinical information about people with diabetes from the unstructured free-text notes.
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