18 articles · 3 topics
Critical care medicine6 articles
Score 0.88 · ✓ Verified
Stéphan Ehrmann et al. — Intensive Care Medicine · July 20, 2026
Why it’s here: This article directly addresses ARDS, mechanical ventilation, and its physiological effects, aligning perfectly with the researcher's interests.
Study Design
This article reviews the evidence for prone positioning in acute respiratory distress syndrome (ARDS). It covers its evolution from a rescue therapy to an integral component of lung-protective ventilation and its expanded use in non-intubated patients.
Key Results
Improved oxygenation is a consistent and well-recognized effect of prone positioning in both intubated and non-intubated ARDS patients. Beyond gas exchange, it mitigates ventilator-induced lung injury by reducing lung stress and strain and may also confer favorable hemodynamic effects.
Why It Matters
Prone positioning is now recognized as an evidence-based intervention and an integral component of lung-protective mechanical ventilation strategies for intubated ARDS patients with a PaO2/FIO2 ratio < 150 mmHg. Its use in non-intubated patients (awake prone position) showed promising results but requires confirmation in non-COVID patients and routine ICU practice.
Score 0.72 · ✓ Verified
Hunter Gage et al. — American Journal of Respiratory and Critical Care Medicine · July 24, 2026
Why it’s here: This article directly addresses severe bacterial pneumonia, inflammation, ARDS, and survival in a critical care context, with potential therapeutic interventions.
Study Design
This study evaluated the therapeutic potential of macrophage membrane-coated nanoparticles (MΦ-NPs) for severe bacterial pneumonia. The research utilized in vitro models with human lung cells and neutrophils, alongside in vivo murine models infected with Pseudomonas aeruginosa (PA) and methicillin-resistant Staphylococcus aureus (MRSA).
Key Results
MΦ-NPs showed potent cytoprotective and anti-inflammatory activity in vitro without harming neutrophil antimicrobial function. In both PA and MRSA pneumonia models, MΦ-NP treatment significantly improved survival, reduced bacterial burden, lowered proinflammatory cytokines, and preserved lung architecture.
Why It Matters
These findings establish MΦ-NPs as a promising host-directed therapeutic strategy for mitigating the deleterious inflammatory sequelae of severe bacterial pneumonia. Quantitative proteomics revealed suppression of inflammatory, coagulation, and fibrotic pathways linked to poor outcomes in human pneumonia and ARDS.
● Preprint · Score 0.70 · ✓ Verified
Aligning Reinforcement Learning with Clinical Practice for Safe Decision Support in Pediatric Sepsis
Bueso, F. G. et al. — medRxiv Preprint · intensive care and critical care medicine · July 21, 2026
Why it’s here: This article directly addresses clinical decision support for pediatric sepsis management in the ICU, including fluid and vasopressor actions, and organ dysfunction.
Study Design
This study developed offline reinforcement learning (RL) policies for pediatric sepsis management in the Pediatric Intensive Care Unit (PICU) using a retrospective cohort of 2,229 episodes. The approach formalized care as a finite-horizon Markov Decision Process (MDP) with joint intravenous fluid and vasopressor actions, incorporating the Phoenix-8 score for intermediate reward shaping.
Key Results
Conservative Q-Learning (CQL) demonstrated stable learning and favorable Fitted Q Evaluation estimates, outperforming Double Deep Q-Networks (DDQN) which showed instability and overestimation. CQL policies achieved high action-level agreement with clinician decisions and reproduced clinically plausible escalation patterns, while 8-hour time-step binning offered the best trade-off between policy performance and granularity.
Why It Matters
The findings highlight time-step size as a crucial design choice in offline RL for healthcare, suggesting that alternatives beyond the conventional 4-hour setup can enhance stability and safety while preserving clinical interpretability. This work provides empirical evidence for more trustworthy clinical artificial intelligence in pediatric sepsis by carefully considering algorithm and design choices.
Score 0.66 · ✓ Verified
Acute pulmonary embolism: clinical and echocardiographic recognition of normotensive shock
Oliver Hunsicker et al. — Critical Care · July 25, 2026
Why it’s here: This article discusses acute pulmonary embolism, normotensive shock, tissue hypoperfusion, and treatment escalation in the context of critical care, directly aligning with hemodynamic support and ICU outcomes.
Study Design
This article discusses the clinical and echocardiographic recognition of normotensive shock in acute pulmonary embolism. It emphasizes the need for reliable, rapidly obtainable, and broadly applicable criteria for early recognition of this pre-cardiopulmonary failure state.
Key Results
Shock assessment should integrate concordant clinical signs of tissue hypoperfusion, such as prolonged capillary refill time and skin mottling, which may complement lactate as bedside markers. Critical care echocardiography can confirm acute right ventricular pressure overload, exclude alternative causes of circulatory failure, and assess right ventricular adaptation and coupling.
Why It Matters
Early recognition of normotensive shock may identify patients at imminent risk of deterioration who could benefit from timely treatment escalation, including consideration of reperfusion therapy. This approach aligns with the 2026 AHA/ACC guideline, which introduces normotensive shock as clinically relevant with implications for treatment escalation.
● Preprint · Score 0.66 · ✓ Verified
Cross-database validation reveals distinct layers of transportability in ICU delirium prediction
Ni, S., Sato, K. — medRxiv Preprint · health informatics · July 21, 2026
Why it’s here: This article discusses AI for delirium prediction in the ICU, focusing on transportability of models and clinical outcomes, highly relevant to ICU outcomes and AI in critical care.
Study Design
This study retrospectively evaluated five model families across eICU and MIMIC-IV to assess the transportability of delirium prediction models. The evaluation examined distinct layers of transportability, including discrimination, probability estimates, and operating policies, revealing divergences between internal and external validation.
Key Results
Coarse-label AUROC dropped from 0.87-0.92 internally to 0.66-0.83 during source-only transfer, but external AUROC reached 0.76-0.94 when assessment history was considered for repeated monitoring. Removing assessment history reduced external AUROC by 0.16-0.32, and broader features did not consistently improve transportability. Transported scores concentrated future-positive ICU stays 2.4-6.9-fold in the top risk decile.
Why It Matters
The findings demonstrate that while ranking performance can persist across sites, probability estimates and operating policies remain site-dependent, highlighting the limitations of relying solely on discrimination for external validation. Layered validation is a prerequisite for prospective evaluation and is not evidence of clinical benefit.
● Preprint · ✦ Something Different · Score 0.53 · ✓ Verified
Lynch, N. et al. — medRxiv Preprint · genetic and genomic medicine · July 20, 2026
✦ A change of pace: This article explores pharmacogenomic guidelines for medications in critically ill children, offering a novel genetic and genomic perspective distinct from the reader's typical focus on hemodynamics and organ support.
Study Design
This retrospective cohort study integrated electronic medical record and exome sequencing data from a single tertiary care children's hospital. The study included 4,939 children admitted to the PICU and 192 PICU patients who underwent exome sequencing for research purposes.
Key Results
Among 4,939 PICU patients, 37.2% received at least one medication with established PGx guidelines, and 14.4% received two or more. Among 192 patients with exome sequencing, 62% had at least one identifiable metabolizer phenotype, and an estimated 8.2% of all PICU patients received medications for which PGx-guided recommendations would have altered clinical management.
Why It Matters
Many critically ill children receive medications with established PGx guidelines, representing a missed opportunity for personalized medicine. This study highlights the potential for applying precision medicine in pediatric intensive care and assesses the utility of exome sequencing for uncovering relevant PGx phenotypes.
Healthcare machine learning6 articles
★ Flagship · Score 0.99 · ⓘ Reference
AI for Proactive Mental Health: A Multi-Institutional, Longitudinal Randomized Controlled Trial
Julie Youko Anne Cachia et al. — NEJM AI · July 22, 2026
Why it’s here: This article directly addresses AI for proactive mental health using a randomized controlled trial, fitting the interest in clinical care and validation.
Abstract unavailable — listed as a pointer; not summarized.
Score 0.78 · ✓ Verified
Li Fan et al. — npj Digital Medicine · July 20, 2026
Why it’s here: This article develops and validates a multimodal deep survival model for time-varying risk prediction after cancer resection, aligning with predictive models and clinical decision support.
Study Design
This study developed and externally validated TEMPO-HCC, a multimodal deep survival model with hierarchical interpretability, to estimate individualized overall survival risk trajectories after curative-intent resection for hepatocellular carcinoma. The model integrated multiphasic MRI, postoperative H&E whole-slide images, and perioperative predictors from a six-center cohort of 1475 patients.
Key Results
TEMPO-HCC outperformed unimodal models and guideline-based staging systems, achieving a C-index of 0.751 and time-dependent AUCs of 0.836, 0.781, 0.812, and 0.680 at 12, 24, 36, and 60 months, respectively, in the external validation cohort. A discrete-time survival head generated probabilities at 1, 2, 3, and 5 years after surgery.
Why It Matters
TEMPO-HCC represents a paradigm shift toward clinically actionable temporal risk prediction, offering an auditable evidence chain linking radiologic to histopathologic patterns. Augmenting guideline staging with TEMPO-HCC improved discrimination, enabling personalized postoperative surveillance and risk-adapted clinical management.
Score 0.76 · ✓ Verified
A multimodal evidence-driven framework for clinical decision support in cognitive impairment
Shicong Hu et al. — npj Digital Medicine · July 20, 2026
Why it’s here: This article presents a multimodal deep learning framework with retrieval-augmented LLMs for clinical decision support in cognitive impairment, directly matching the stated interests.
Study Design
This study developed the Multimodal Evidence-Driven Reasoning Framework (MEDRF), which integrates a Multimodal Hierarchical Cascade (mHC) classifier with a retrieval-augmented large language model (RAG-LLM). MEDRF utilizes routinely collected non-invasive data from clinical profiles and structural MRI to identify cognitive impairment stages and etiologies.
Key Results
The mHC classifier outperformed flat multimodal baselines across 15 diagnostic labels, and integrating RAG-LLM with mHC improved overall accuracy from 0.706 ± 0.038 to 0.753 ± 0.032 in external validation. RAG-LLM correction mitigated performance decline under feature masking, especially with severe sparsity.
Why It Matters
MEDRF provides a robust and interpretable framework for clinical decision support in cognitive impairment, particularly for incomplete or ambiguous cases, by synthesizing hierarchical prediction with evidence-grounded reasoning. Physician review indicated favorable quality and perceived usefulness of the generated reports.
Score 0.76 · ✓ Verified
Large language models for interpretation of health checkup results
JeongBong You -, Hang‐Sik Shin — npj Digital Medicine · July 22, 2026
Why it’s here: This article evaluates large language models for interpreting health checkup results, directly matching the interest in LLMs in medicine and data interpretation.
Study Design
This study evaluated four large language models (LLMs) – Claude Sonnet 4, Gemini 2.5 Pro, GPT-4o, and LLaMA 3.1-70B – using comprehensive health checkup data from the Korean National Health Insurance Service. Multiple prompting strategies, including zero-shot, few-shot, role-based, constraint-based, and Chain-of-Thought, were tested to assess their interpretive capabilities.
Key Results
Zero-shot accuracy averaged 0.69, significantly increasing to 0.95 with Chain-of-Thought prompting. Claude Sonnet 4, Gemini 2.5 Pro, and GPT-4o achieved the highest accuracies (≥ 0.98), demonstrating near-perfect interpretation for most biochemical markers but lower accuracy for blood pressure (0.61-0.91).
Why It Matters
Advanced prompting strategies markedly improved LLM performance in interpreting health checkup data, with top-tier models showing robust capabilities for many variables. However, caution is advised for variables with complex clinical semantics like blood pressure, and model-specific biases related to sex and age were observed.
Score 0.76 · ✓ Verified
Evaluating large language models for assessment of psychosis risk
Taiyu Zhu et al. — npj Digital Medicine · July 23, 2026
Why it’s here: This article evaluates large language models for assessing psychosis risk, directly aligning with predictive models and LLMs in medicine for clinical care.
Study Design
This study evaluated the ability of 11 open-weight large language models (LLMs) to extract clinically meaningful information from partial PSYCHS interview transcripts for psychosis risk assessment. The dataset comprised 678 transcripts from 373 participants, with 77.7% identified as clinically high risk for psychosis (CHR-P). LLMs were assessed on their capacity to infer CHR-P status and estimate symptom severity and frequency, benchmarked against researcher ratings.
Key Results
Larger LLMs demonstrated superior classification performance, with Llama-3.3-70B achieving an accuracy of 0.80 and a sensitivity of 0.93. LLM-generated symptom severity and frequency scores showed good correlations with researcher ratings (ICC sev = 0.74, ICC freq = 0.75). While performance disparities were minimal across most demographic groups, they varied across sites, and errors primarily involved over-pathologisation of non-clinical experiences.
Why It Matters
These findings suggest that open-weight LLMs hold potential for supporting scalable, human-in-the-loop psychosis risk assessment by analyzing psychometric interview transcripts. Although accuracy scales with model size, smaller models offered competitive performance at a lower computational cost. A limitation noted was a low rate of clinically relevant confabulation (3%) in generated summaries.
✦ Something Different · Score 0.48 · ✓ Verified
Qin Lin et al. — BMC Health Services Research · July 23, 2026
✦ A change of pace: This qualitative study offers a unique perspective on user experience with generative AI in healthcare, shifting focus from technical performance to patient interaction and decision-making in complex health scenarios.
Study Design
This qualitative study explored generative AI use for complex health decisions among older adults with multimorbidity. Sixteen participants were recruited from an urban community health center in eastern China and interviewed using semi-structured, face-to-face methods.
Key Results
Four themes emerged: perceived usefulness (information access, multimorbidity management, decision confidence), perceived ease of use (individual differences, context dependence), resource mobilization (personal and social support), and dynamic usage behaviors (attempts, interruptions, resumption, or abandonment). Successful AI use rarely depended on independent effort but required mobilizing diverse resources.
Why It Matters
The study concludes that AI use among older adults with multimorbidity is an adaptive strategic choice, not just technology adoption. Safe and effective AI application in elderly chronic disease management requires considering the special needs of multimorbid patients in technology design and service provision.
Hospital logistics and operations6 articles
Score 0.72 · ✓ Verified
How access to long-term care affects short-term care
Ismail Aydemir et al. — BMC Health Services Research · July 22, 2026
Why it’s here: This article directly addresses hospital bed allocation, waiting times, and the impact of different admission policies on short-term and long-term care, which are core hospital operations and logistics topics.
Study Design
This study used discrete-event simulation to model the admission trajectories of older adults requiring nursing home care in the Netherlands. The model analyzed client arrivals and admissions/discharges across both long-term and short-term care settings. It compared a decentralized admission policy to a centralized one using waiting times and short-term care admission rates as metrics.
Key Results
A centralized admission policy significantly reduced waiting times and short-term care admissions, dropping waiting times from 336 to 35 days and short-term care admissions from 45% to 4.5% in the base case scenario. These reductions were even more pronounced when demand variation across nursing homes was higher.
Why It Matters
The findings demonstrate that long waiting times and uneven demand in long-term care increase short-term care admissions, thereby straining the system. Efficient bed allocation is most effective when the system operates near full capacity, highlighting the interdependency between long-term and short-term care services.
Score 0.68 · ✓ Verified
Untangling standardization and administrative burden as microfoundations of agility in healthcare
Pierre‐Luc Fournier et al. — BMC Health Services Research · July 23, 2026
Why it’s here: This article investigates process standardization and administrative burden as microfoundations of agility in healthcare, directly relating to hospital operations and efficiency.
Study Design
This study investigates the tensions between standardization, administrative burden, and individual agility in healthcare. Using survey data from 2,173 Canadian nurses, structural equation modelling was employed to assess a hypothetical model.
Key Results
The quality of care provided by nurses is increased by their individual agility, which is positively influenced by process standardization but inhibited by administrative burden. Specifically, individual agility positively influences the quality of care.
Why It Matters
This study extends literature on the microfoundations of agility in healthcare by providing theoretical and practical contributions on the impact of enabling and coercive bureaucracy. The findings highlight how process standardization can enable agility, while administrative burden can inhibit it, impacting care quality.
★ Flagship · Score 0.67 · ✓ Verified
Navid Izady, Sergei Savin, Reza Zanjirani Farahani — Manufacturing & Service Operations Management · July 24, 2026
Why it’s here: This article uses optimization models and operations research to address facility specialization and demand management in healthcare, directly aligning with hospital operations and logistics.
Study Design
This study investigates how a network of healthcare facilities can manage concurrent pandemic and non-pandemic demand by comparing specialized versus generalized facility operating modes. The research develops two optimization models to minimize patient waiting and infection mitigation costs under static demand allocation.
Key Results
The models suggest that the optimal configuration includes at most one generalized facility, and a lower bound on the value of specialization is provided. For COVID-19 data from England and the Netherlands, this lower bound was 6.5% and 21.2%, respectively, indicating substantial potential value.
Why It Matters
These findings highlight the considerable potential value of facility specialization in reducing costs during health emergencies. While static allocation can be effective, dynamic policies like virtual pooling only offer savings at very high traffic intensities, and optimal static policies can significantly reduce costs when combined with capacity expansion.
Score 0.66 · ✓ Verified
Cross-training of nurses during a global pandemic: a two-stage stochastic programming approach
Hendrik Winzer, Jens Bengtsson — Health Care Management Science · July 20, 2026
Why it’s here: Directly addresses nurse staffing and cross-training using stochastic programming for pandemic preparedness, highly relevant to hospital operations and OR.
Study Design
This study proposes a two-stage stochastic programming model to address nurse staffing challenges during a global pandemic, specifically focusing on uncertain patient influx and personnel absenteeism. The model optimizes nurse allocation by considering tactical decisions on cross-training and operational decisions on hiring temporary nurses.
Key Results
Applied to data from a Norwegian tertiary public hospital during the COVID-19 pandemic's first wave, the model identified a bottleneck in intensive care unit nurse availability. Sensitivity analyses indicated that increasing the penalty for untreated patients had a significantly larger effect than changes in cross-training parameters or costs.
Why It Matters
The findings suggest that cross-training is advantageous for reducing bottlenecks and improving future service levels, even though it temporarily reduces nurse availability. This study is the first to model cross-training as a tactical staffing decision while accounting for absenteeism risk, offering valuable insights for nurse staffing strategies despite its limited scope.
Score 0.66 · ✓ Verified
Leena Ghrayeb et al. — Health Care Management Science · July 27, 2026
Why it’s here: This article directly addresses operational impacts, scheduling, and patient flow optimization within a healthcare setting using simulation and optimization models.
Study Design
This study proposes a discrete-event simulation model embedded with a mixed-integer linear programming model to analyze the operational impacts of tailored prenatal care pathways. The model captures patient heterogeneity and schedules patients weekly to minimize patient delays, rescheduling, and overbooking. It was applied to a case study of a single prenatal care clinic within a large academic health center.
Key Results
Results indicate that tailoring care significantly reduces delays, rescheduling, and overbooking compared to traditional approaches. Specifically, scheduling appointments one at a time, rather than by trimester or the entire pathway, yields schedules with minimal delays and overbooking.
Why It Matters
This research demonstrates that adopting tailored prenatal care can improve clinic efficiency, potentially allowing for increased patient capacity or better adaptation to social risks and complications. The findings highlight the importance of granular scheduling policies for optimizing operational outcomes in prenatal care settings.
● Preprint · ✦ Something Different · Score 0.58 · ✓ Verified
Tyagi, A. et al. — medRxiv Preprint · health informatics · July 23, 2026
✦ A change of pace: This article explores the limitations of AI in healthcare triage, offering a novel perspective on the intersection of technology and patient care that differs from traditional operational studies.
Study Design
This study retrospectively evaluated 255 patient cases, including physician-authored vignettes, real-world emergency department cases, and nurse line cases, to assess ChatGPT's triage recommendations. The evaluation compared single-turn and multi-turn interactions against nurse line standards and clinician-adjudicated dispositions.
Key Results
Exact agreement with nurse line standards was 52.9% for single-turn and 55.7% for multi-turn interactions, with clinician-adjudicated disposition agreement at 54.1% and 48.2% respectively. Discordant recommendations frequently represented lower acuity, with 70.8% under-triage in natural (single-turn) use and 69.0% in multi-turn use, both statistically significant (P < 0.0001).
Why It Matters
These findings indicate that conversational interaction does not guarantee safe triage-disposition alignment for AI systems like ChatGPT Health. Future evaluations should consider ordinal distance from standards, error direction, and dialogue conditions beyond aggregate agreement to ensure safe clinical AI deployment.
Curated from OpenAlex, Crossref & medRxiv. Summaries are machine-generated and grounded in each article’s abstract or open-access text; ⚠ flags a summary that needs a second look, and ✦ marks a deliberate change-of-pace pick.
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