18 articles · 3 topics
Critical care medicine6 articles
Score 0.94 · ⓘ Reference
Ignacio Martín‐Loeches et al. — Intensive Care Medicine · August 13, 2026
Why it’s here: Focuses on randomized controlled trials and changes in intensive care medicine over decades.
Abstract unavailable — listed as a pointer; not summarized.
Score 0.94 · ⓘ Reference
Frédéric Pène et al. — Intensive Care Medicine · August 19, 2026
Why it’s here: This article is a randomized controlled trial on early red blood cell transfusion strategy in septic shock, directly matching the researcher's interest in sepsis and RCTs in intensive care.
Abstract unavailable — listed as a pointer; not summarized.
Score 0.76 · ⚠ Check
Dongwook Lee et al. — Critical Care Medicine · August 17, 2026
Why it’s here: This article directly addresses sepsis incidence, outcomes, and mortality in a large cohort, aligning perfectly with the researcher's interest.
⚠ a section is not grounded in the abstract (The 'Study Design section' contains the sentence 'Adult patients hospitalized for sepsis, identified by ICD-10 codes, were included in the analysis.' While the abstract mentions 'Adult patients 18 years old or older hospitalized for sepsis and identified using the International Classification of Diseases, 10th Revision codes,' it does not explicitly state that these patients were 'included in the analysis.' This is an inference, not a direct statement.)
Study Design
This retrospective, population-based cohort study utilized South Korea's National Health Insurance Service database from 2011 to 2022. Adult patients hospitalized for sepsis, identified by ICD-10 codes, were included in the analysis. Temporal trends and risk factors for in-hospital mortality were assessed using regression models.
Key Results
The crude incidence of sepsis increased from 141.8 to 337.3 per 100,000 person-years, while age-standardized incidence rose by 53.1%. Concurrently, in-hospital mortality significantly decreased from 27.0% in 2011 to 17.0% in 2022, though the total number of deaths increased. Older age, male sex, higher comorbidity burden, and greater disease severity were identified as independent risk factors for mortality.
Why It Matters
Recorded sepsis incidence increased and in-hospital mortality declined over time, but these trends require cautious interpretation due to potential shifts in case identification. Socioeconomic disparities persisted, with medical aid beneficiaries and rural residents experiencing higher mortality, while tertiary hospital treatment was linked to better outcomes. These findings highlight persistent socioeconomic disparities in sepsis mortality.
● Preprint · Score 0.76 · ⚠ Check
Generalizing intensive care AI across time scales in resource-limited settings
Devadiga, A. et al. — medRxiv Preprint · health informatics · August 12, 2026
Why it’s here: This article directly addresses the generalization of AI in critical care settings, focusing on time scales and resource limitations, highly relevant to the researcher's interest.
⚠ a section is not grounded in the abstract (Key Results section: The sentence 'These representations also generalized across independent adult intensive care cohorts, achieving AUROC values of 0.91-0.95 and AUPRC values of 0.94-0.97.' is not directly and unambiguously supported by the abstract. The abstract states that representations learned at high temporal resolution demonstrated strong transferability across lower monitoring frequencies, consistently outperforming models trained directly on temporally aggregated data, and importantly, these representations generalized across patient populations, maintaining performance when evaluated on independent adult intensive care cohorts derived from the MIMIC-III and eICU databases without retraining. It also states that representations trained on pediatric data generalized to independent adult ICU cohorts, achieving AUROC 0.91-0.95 and AUPRC 0.94-0.97 without cohort-specific fine-tuning. However, the Key Results section combines these two points into a single sentence, implying that the AUROC and AUPRC values mentioned are for the generalization to adult cohorts, which is supported, but the initial part of the sentence about transferability across lower monitoring frequencies is not directly linked to these specific AUROC/AUPRC values in the abstract. The abstract separates these findings.)
Study Design
This study introduces PhysioStack, a self-supervised physiological representation learning framework designed to evaluate transferability across monitoring frequencies. The framework was trained on high-resolution physiological signals from the SafeICU pediatric intensive care dataset and evaluated for its ability to generalize without retraining.
Key Results
Representations learned at high temporal resolution demonstrated strong transferability across lower monitoring frequencies, consistently outperforming models trained directly on temporally aggregated data. These representations also generalized across independent adult intensive care cohorts, achieving AUROC values of 0.91-0.95 and AUPRC values of 0.94-0.97.
Why It Matters
These findings demonstrate that PhysioStack learns transferable physiological representations that generalize across temporal resolutions and patient cohorts, supporting robust AI for critical care. The public release of the SafeICU database and pretrained PhysioStack models aims to accelerate future research on representation learning and foundation models for critical care.
Score 0.70 · ✓ Verified
Stephan von Düring et al. — Critical Care Medicine · August 12, 2026
Why it’s here: Examines epidemiology, ventilatory patterns, and outcomes in acute hypoxemic respiratory failure in the ICU, directly matching the researcher's interests.
Study Design
This multicenter registry-based cohort study, conducted from 2014-2023 across nine university-affiliated ICUs, examined adult patients requiring supplemental oxygen or respiratory support for at least 4 hours. Patients meeting AHRF criteria, defined by Pao2/Fio2 ≤ 300 mm Hg or SpO2/Fio2 ≤ 315, within 24 hours of ICU admission were included. No specific interventions were applied.
Key Results
Of 21,714 registered patients, 10,832 (50%) met AHRF criteria, with 76% requiring invasive mechanical ventilation (IMV) and most receiving lung-protective ventilation. Increasing AHRF severity correlated with longer ICU stays and IMV durations, fewer ventilator-free days, and a decreased probability of ICU discharge at 30 days. Overall ICU mortality was 23%, rising to 47% for IMV patients with severe AHRF.
Why It Matters
This study highlights that AHRF is common upon ICU admission and often managed with ARDS-based strategies, underscoring the need for a clear definition to improve recognition and facilitate research comparisons. The findings demonstrate a clear association between increasing AHRF severity and worse outcomes, including significantly higher ICU mortality. A pragmatic, standardized definition of AHRF is proposed to guide future research and clinical practice.
✦ Something Different · Score 0.36 · ✓ Verified
Furaha Nzanzu Blaise Pascal et al. — BMC Health Services Research · August 13, 2026
✦ A change of pace: This article explores the knowledge, attitudes, and practices related to sepsis management in a specific, under-resourced region, offering a unique public health and global perspective distinct from the more clinically focused articles.
Study Design
This cross-sectional KAP survey was conducted from January to February 2025 in the Health Antenna of Butembo, Democratic Republic of Congo. The study included 152 medical doctors, primarily general practitioners, who voluntarily participated by completing a questionnaire.
Key Results
A significant majority of medical doctors (88.2%) demonstrated very low or low knowledge of sepsis, although 81.6% showed a positive attitude towards its care. Only 11.3% of the 53 assessed health facilities were deemed ready to provide sepsis care, with only 30.9% of doctors systematically practicing sepsis screening.
Why It Matters
Despite doctors' positive attitudes and willingness to learn, substantial gaps in knowledge, practice, and health facility readiness highlight the need for context-adapted training and system strengthening to improve sepsis management. The study's findings underscore critical areas for intervention to enhance sepsis care outcomes in this low-resource setting.
Healthcare machine learning6 articles
★ Flagship · Score 0.87 · ⓘ Reference
Liron Leibovitch et al. — Nature Medicine · August 19, 2026
Why it’s here: Directly addresses prospective evaluation of an LLM clinical decision support system in a clinical setting.
Abstract unavailable — listed as a pointer; not summarized.
Score 0.78 · ✓ Verified
Anran Li et al. — npj Digital Medicine · August 15, 2026
Why it’s here: Directly addresses federated large language models in medicine for adaptation across healthcare institutions, focusing on privacy and generalization.
Study Design
This study introduces Fed-MedLoRA and Fed-MedLoRA+, a parameter-efficient federated framework for collaborative LLM adaptation across healthcare institutions. The framework transmits low-rank adapters and includes a privacy-preserving variant with Gaussian perturbation, alongside adaptive aggregation to handle cross-site heterogeneity.
Key Results
The framework was evaluated on clinical information extraction across five independent patient cohorts, consistently improving extraction performance and generalization compared to zero-shot, fine-tuned LLMs, and federated baselines. In a case study with the Yale New Haven Health System, it demonstrated strong performance under low-resource new-site deployment.
Why It Matters
These findings suggest that federated, parameter-efficient LLM adaptation is feasible, scalable, and effective for multi-institutional clinical deployment, addressing the generalization gap caused by single-institution training due to privacy constraints. The methods enable collaborative LLM adaptation without requiring multi-institutional data sharing.
Score 0.78 · ✓ Verified
Automated limb motor assessment in Parkinson’s disease via a time-frequency state-space model
Tianyao Zhang et al. — npj Digital Medicine · August 12, 2026
Why it’s here: This paper presents a machine learning framework for automated motor assessment in Parkinson's disease, including prospective validation and improved clinical agreement.
Study Design
This study developed TF-Mamba, a time-frequency dynamics-aware framework for automated limb motor assessment in Parkinson's disease, integrating a spatiotemporal feature backbone with a physical signal dynamics module. The model was trained on data from 1479 Parkinson's disease patients across four Chinese centers and validated on 450 patients from three centers.
Key Results
TF-Mamba achieved an AUC of up to 0.90 in external validation for five-level severity scoring, outperforming state-of-the-art baselines by 8–10%. Specific metrics like Energy Entropy, Amplitude Coefficient of Variation, and Frequency Stability were significantly associated with clinical severity (all p < 0.001).
Why It Matters
The TF-Mamba framework offers an interpretable and scalable solution for MDS-UPDRS limb motor assessment, reducing evaluation time by 60–70% and improving junior physicians' rating agreement with expert consensus. This automated approach addresses limitations of subjective visual inspection and existing deep learning methods in characterizing non-stationary and nonlinear movement dynamics.
Score 0.72 · ✓ Verified
Clinicians vs. artificial intelligence in patient outcome prediction in the intensive care unit
Corin Kuang et al. — npj Digital Medicine · August 12, 2026
Why it’s here: Compares clinician and AI predictive performance in the ICU, including prospective real-world settings and clinical decision-making.
Study Design
This mixed retrospective and prospective study evaluated the predictive performance of human clinicians and artificial intelligence (AI) in 15 adult ICUs in Alberta, Canada. The study included retrospective data from 990 ICU admissions and prospective data from 238 ICU admissions, with AI models trained on a larger dataset of 46,631 ICU admissions.
Key Results
In the retrospective setting, aggregated clinician predictions outperformed AI, while in the prospective setting, subspecialized physicians outperformed AI, but physicians in training and nurses underperformed. Both clinicians and AI demonstrated poor performance in predicting length of stay, and inter-rater agreement was generally poor or fair.
Why It Matters
This study establishes important prediction performance benchmarks for clinicians and AI in both retrospective and prospective ICU settings, highlighting the variability in clinician performance based on specialization and training level. A key limitation noted is the poor performance of both clinicians and AI in predicting length of stay.
Score 0.72 · ✓ Verified
Ariel Yuhan Ong et al. — npj Digital Medicine · August 13, 2026
Why it’s here: Develops a scalable pipeline using LLMs for data extraction from clinical letters, focusing on real-world deployment and performance.
Study Design
This study developed and tested a scalable, resource-efficient pipeline for extracting information from free-text clinical records using large language models (LLMs). The pipeline was developed and validated using real-world dual specialist-annotated ophthalmic clinical letters.
Key Results
The pipeline achieved a maximum micro-averaged F1 score of 0.954 (95% CI 0.941–0.967) for diagnosis across nine conditions through iterative prompt refinement alone, demonstrating strong generalisability with micro-F1 scores ranging from 0.945–0.980 in temporal validation. This performance was robust across multiple proprietary and 17 local models from seven open-weight LLM families (for models > 10B parameters).
Why It Matters
This approach offers a robust method for operationalizing LLMs in real-world workflows at scale, potentially accelerating scientific discovery and powering continuous learning health systems. The study also introduced a multi-dimensional assessment for LLM deployment, including an error taxonomy and Pareto frontier analyses for operational trade-offs.
✦ Something Different · Score 0.54 · ✓ Verified
Jade Buford et al. — BMC Health Services Research · August 12, 2026
✦ A change of pace: This article explores the use of AI tools in qualitative research, offering a novel perspective on AI's application beyond quantitative prediction and diagnosis.
Study Design
This study employed two exploratory case studies to compare generative AI tools against human researchers in qualitative research within kidney transplantation. The first case study evaluated AI transcription using LEXI Recorded on 14 semi-structured interviews, while the second assessed AI thematic analysis using Microsoft Copilot on 616 open-ended survey responses.
Key Results
AI transcription achieved an average character error rate of 1.77%, saving an estimated four hours and $362, but performed worse in formatting, flow, speaker identification, accuracy, and redaction. While AI thematic analysis showed overlapping themes with manual coding, manual analysis provided context-sensitive insights and synthesis across social and systemic factors, whereas AI-generated themes were compartmentalized with limited connection to broader implications.
Why It Matters
Contemporary AI tools offer significant efficiency gains in transcription and analysis but currently fall short in contextual richness and capturing complex human experiences. Findings support using structured, researcher-led hybrid human-generative AI workflows to ensure ethical and rigorous qualitative research.
Hospital logistics and operations6 articles
Score 0.84 · ✓ Verified
EXPRESS: Impact of Patient Navigation Programs on Emergency Department Utilization
Jiajia Qu, Raj Sharman, Indranil R. Bardhan — Production and Operations Management · August 18, 2026
Why it’s here: This article directly addresses the impact of patient navigation programs on emergency department utilization, a core aspect of hospital operations and patient flow.
Study Design
This study examines the impact of patient enrollment in a patient navigation (PN) program on emergency department (ED) utilization within a large Accountable Care Organization (ACO). The research employs a staggered difference-in-differences design combined with sliding-window, propensity score matching using longitudinal, patient-level data. Qualitative insights from healthcare practitioners were also incorporated to understand underlying mechanisms.
Key Results
PN enrollment was found to significantly reduce ED visit utilization, though this effect diminishes over time, indicating decreasing marginal returns to prolonged PN engagement. While reductions in ED utilization generate measurable cost savings, these savings alone do not fully offset per-patient program costs.
Why It Matters
This research contributes to operations management by revealing the dynamic effectiveness of care coordination interventions over time and across patient populations, offering practical insights for designing more targeted and cost-effective PN programs. The findings suggest that the full value of PN programs extends beyond ED utilization reductions to other aspects of patient care and care coordination, particularly within value-based care reimbursement settings.
★ Flagship · Score 0.73 · ✓ Verified
Customers’ Multihoming Behavior in Ride-Hailing: Empirical Evidence from Uber and Lyft
Sandeep Chitla et al. — Manufacturing & Service Operations Management · August 13, 2026
Why it’s here: This article uses operations research (queueing/choice models) to analyze customer behavior in ride-hailing, which is highly relevant to the application of operations research to service delivery, analogous to hospital logistics.
Study Design
This study investigates customer multihoming behavior in the ride-hailing market using a large panel dataset of over 1.4 million Uber and Lyft rides from 162 thousand riders in NYC during 2018. A comprehensive structural model was developed to explain rider choices, incorporating operational factors like price and waiting time, alongside behavioral elements such as platform stickiness and Bayesian belief updating.
Key Results
While 83.4% of riders exclusively used a single platform, the study found that even among the 16.6% who used both Uber and Lyft, they considered both platforms only 43.4% of the time. This suggests that customers view platforms as differentiated service providers rather than a pure commodity.
Why It Matters
These findings highlight the importance of understanding customer multihoming behavior for designing effective pricing strategies, as personalized discounts may fail if a platform is not considered. Targeting customers earlier in their lifecycle or those with low search friction can significantly enhance market share compared to current discounting methods.
Score 0.72 · ✓ Verified
Jeroni Salabert Carreras, Rodolfo de Castro Vila, Marc Sales Coll — BMC Health Services Research · August 12, 2026
Why it’s here: This article proposes a Lean-based Digital Twin model for planning and governance in hospital networks, directly addressing capacity and patient flow.
Study Design
This study proposes a Lean-grounded Digital Twin (DT) model for integrated activity and capacity planning across multi-provider hospital networks. The model's scope and capabilities were derived from a synthesis of Lean improvement projects in over 50 hospitals and refined through expert review with senior healthcare leaders. It is situated within the planning phase of a broader Action Research approach.
Key Results
The proposed four-layer conceptual DT architecture operates at both individual-hospital and network management levels, representing four interrelated macro-processes (ambulatory, surgical, inpatient, and emergency) as interconnected systems. It standardizes core entities, operational variables, and transition probabilities into a unified virtual representation, enabling scenario analysis for tactical contracting, bottleneck detection, and strategic resource allocation.
Why It Matters
This model extends hospital management beyond traditional hospital-centric optimization by integrating Lean process orientation with the DT paradigm, offering a data-driven framework for territorial health authorities. It supports a shift from reactive, historically based contracting to more proactive, value-based network management. The study provides an architectural basis for future empirical implementations of Digital Twins in complex territorial health networks.
● Preprint · Score 0.62 · ✓ Verified
Developing a needs-based workforce plan for audiology services in England
Rajasingam, S. L. et al. — medRxiv Preprint · health policy · August 18, 2026
Why it’s here: Develops a needs-based workforce plan for audiology services, directly addressing staffing and service delivery capacity.
Study Design
This research developed a needs-based model to estimate audiology workforce requirements in England. The study analyzed national stocktake data, created an epidemiological model for population changes over 5 and 10 years, and used British Academy of Audiology (BAA) endorsed estimates for staff grades per activity.
Key Results
For adult audiology, Model 1 estimates a 7.40% increase in whole time equivalent (WTE) staff to 1125.18 by 2035. Paediatric audiology projections varied, with Model 1 predicting a 6.3% decrease to 593.47 WTE, while Model 2 and Model 3 estimated increases of 71.23% (to 1072.33 WTE) and 59.17% (to 996.82 WTE) respectively.
Why It Matters
This study is the first to conduct a needs-based assessment for audiology workforce requirements, highlighting a substantial need for staff. Investment in recruitment and training is essential to meet future population needs, with consideration for changing demographics in workforce specialization planning.
Score 0.60 · ✓ Verified
When supply chains fail: rethinking resilience in intensive care
Clara Del Prete, Luca Carenzo, Marta Caviglia — Critical Care · August 12, 2026
Why it’s here: This article directly addresses supply chain resilience and its impact on ICU operations, which is highly relevant to hospital logistics.
Study Design
This conceptual analysis examines the vulnerability of intensive care units (ICUs) to systemic shocks impacting their supply chains. It traces the cascade from geopolitical, environmental, and technological disruptions to bedside care, highlighting the role of efficiency-driven reforms in reducing organizational slack.
Key Results
The analysis reveals that highly optimized ICU processes are threatened by geographically concentrated production and single-site dependencies for critical products, leading to rapid disruption propagation. Decades of efficiency reforms have systematically reduced organizational slack, such as inventory and workforce flexibility, which are crucial for adaptation under stress.
Why It Matters
The findings suggest that resilience should be a design principle for ICUs, not an emergent property of crisis response, as efficiency and resilience are in structural tension. Targeted strategies like regionalized networks, strategic stockpiling, pre-emptive conservation protocols, and digital infrastructure can strengthen ICU resilience.
✦ Something Different · Score 0.52 · ✓ Verified
Ahmed Omar Siyad, Abdukadir Mohamed Hassan — Critical Care · August 18, 2026
✦ A change of pace: This article offers a unique perspective on critical care logistics in extreme, conflict-driven environments, contrasting sharply with typical hospital operations research and broadening the reader's understanding of healthcare system resilience under duress.
Study Design
This Comment reframes critical care readiness in conflict and disaster-affected Somalia as a pathway problem, moving beyond solely assessing intensive care unit capacity. It integrates mass-casualty response, emergency referral, essential emergency and critical care inputs, ethical triage, and non-punitive system learning into a practical readiness agenda.
Key Results
The analysis highlights that preventable mortality in fragile health systems can occur before, during, or after ICU-level care due to mass-casualty events, referral constraints, limited oxygen and blood readiness, constrained operative access, and weak post-resuscitation monitoring. Training and hospital preparedness are deemed insufficient without support from referral communication, command structures, oxygen systems, blood readiness, ward monitoring, and clear escalation criteria.
Why It Matters
Strengthening the emergency-to-critical-care pathway is crucial for improving the continuity of life-saving care in Somalia's fragile health system. This approach can support fair resource allocation and enhance accountability in health systems exposed to conflict and disasters.
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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