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Sepsis continues to be a major contributor to morbidity and mortality among hospitalized patients globally, especially within intensive care and emergency departments, where rapid recognition is essential for improving survival through timely treatment. In recent years, machine learning approaches have gained attention for their ability to predict sepsis onset using routinely collected electronic health record data. This systematic review, conducted in accordance with PRISMA 2020 guidelines, synthesizes evidence from studies published between 2017 and 2025, focusing on model architectures, feature selection and engineering strategies, prediction time horizons, and validation methodologies. Searches across major biomedical and informatics databases identified 67 eligible studies. The included literature shows that logistic regression, ensemble tree-based algorithms, and deep learning models are most frequently applied for sepsis prediction tasks. However, the majority of studies rely on retrospective datasets with internal validation, while only a limited number incorporate prospective or real-world validation frameworks. Overall, although reported model performance is often strong in retrospective analyses, a consistent decline in accuracy is observed when models are evaluated in real clinical environments. These findings highlight that prospective validation and improved generalizability are still underdeveloped areas, underscoring the need for future research to emphasize real-time deployment and robust external validation before clinical integration.

Clinical trial recruitment is hindered by slow, costly, and labor-intensive processes, particularly due to the complexity of eligibility criteria often written in free text. This systematic review examines the use of large language models (LLMs) for matching clinical trial eligibility criteria to electronic health records (EHR). It evaluates zero-shot, few-shot, and fine-tuned LLM approaches, comparing their strengths, limitations, and deployment readiness in supporting patient-trial matching. Thirty-three studies published from 2017 to 2026 were included, with findings showing that zero-shot prompting is most adaptable for simple criteria, few-shot prompting offers consistent reasoning for ambiguous criteria, and fine-tuned models excel in task-specific performance but require labeled data and are less portable. The review concludes that no single approach is optimal for all trial screening tasks, and hybrid workflows combining various methods with human verification are most suitable for clinical use.

Care delivery delays arise from asynchronous interactions among clinical decisions, operational constraints, staffing patterns, facility capacity, and patient communication. These delays are rarely represented within a single predictive framework that captures the hospital as a dynamic multimodal system. Existing delay prediction models often focus on one operational endpoint, such as discharge timing, transport coordination, or procedure scheduling. Such models may require extensive hand-engineered features and may not generalize across units, services, or delay types. This article proposes a conceptual multimodal foundation model for predicting care delivery delays using electronic health record events, operational logs, staff assignments, patient messages, facility capacity indicators, and service queue data. The goal is to describe a reusable model backbone that could support multiple downstream operational prediction tasks. The proposed model would use a transformer-based architecture pre-trained through self-supervised learning over heterogeneous temporal hospital data. Task-specific prediction heads could then be fine-tuned for medication delays, procedure delays, discharge delays, transport delays, and broader care progression bottlenecks. Conceptually, the model would learn a holistic representation of clinical workflow, operational pressure, staffing context, patient communication burden, and service demand. It would be expected to produce dynamic delay risk estimates that adapt to changing hospital conditions and tolerate incomplete modality availability. A multimodal foundation model could unify delay prediction across multiple operational domains. Such an approach may support proactive hospital management by transforming fragmented data streams into shared, contextualized representations of care delivery risk.