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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.

Public health emergencies reveal critical weaknesses in healthcare supply chains, especially when PPE demand outpaces procurement and distribution capacity, making predictive analytics an important tool for forecasting demand and improving allocation during crises. This systematic review evaluates predictive analytics models for PPE demand forecasting and distribution optimization during public health emergencies, focusing on model types, data sources, validation approaches, performance metrics, equity considerations, and implementation readiness. Following PRISMA 2020 guidelines, searches were conducted in PubMed, Web of Science, Scopus, IEEE Xplore, and Google Scholar for studies published between 2017 and 2025, yielding 2,847 records, of which 35 met inclusion criteria. Included studies comprised time series and statistical models (34%), machine learning and hybrid approaches (29%), optimization methods (26%), and simulation or digital twin frameworks (11%), with limited evidence of real-world deployment. Overall, findings indicate that predictive analytics can enhance PPE supply chain resilience by improving demand forecasting, allocation decisions, and scenario testing, but widespread adoption is limited by poor data interoperability, insufficient prospective validation, weak equity integration, and limited operational integration into healthcare decision systems.

Federated and decentralized machine learning offer the potential to extract valuable healthcare insights from siloed data without requiring the centralization of sensitive patient records, addressing long-standing privacy and governance challenges. This critical review assesses federated learning in healthcare through three lenses: privacy-preserving technologies, incentive mechanisms, and regulatory compliance frameworks. It examines whether the claims in existing literature are substantiated by real-world evidence from healthcare settings. The review reveals considerable enthusiasm for federated learning but identifies gaps, including incomplete implementation of privacy technologies, theoretical incentive mechanisms, and regulatory compliance often assumed but not validated. Additionally, real-world deployments are limited in scale and duration. The review concludes that the gap between federated learning's theoretical potential and clinical application remains significant, with overstated privacy claims and a lack of established frameworks for incentives and compliance.

Healthcare workforce management faces persistent challenges, including staffing shortages, clinician burnout, skill misalignment, productivity pressure, and turnover. Machine learning has been proposed as a way to support earlier prediction, better allocation, and more responsive workforce decision-making. This systematic review synthesizes peer-reviewed evidence from 2017 to 2025 on machine learning applications in healthcare workforce analytics. The review focuses on staffing prediction, burnout detection, skill matching, productivity monitoring, and retention risk forecasting. A PRISMA 2020-aligned review approach was used to structure database searching, screening, eligibility assessment, and narrative synthesis. The review considered studies from PubMed, Scopus, IEEE Xplore, and Web of Science, with emphasis on workforce-relevant outcomes and healthcare operational settings. Staffing prediction and burnout detection were the most developed domains in the reviewed literature. Skill matching and productivity monitoring were less mature, while retention risk forecasting showed growing interest but limited implementation evidence. Machine learning offers useful tools for healthcare workforce analytics, but the evidence base remains uneven. Stronger prospective validation, fairness assessment, and implementation reporting are needed before these models can be considered mature workforce decision-support systems.

Many patients discharged from emergency departments require timely outpatient follow-up to complete diagnostic, therapeutic, or monitoring plans. When follow-up is delayed, unresolved symptoms, missed diagnoses, medication problems, and preventable return visits may occur. Current discharge workflows often rely on generic instructions and assume that patients can understand, schedule, and attend recommended care. Existing prediction approaches do not consistently combine unstructured discharge instructions, appointment access, portal engagement, social risk, and visit severity in a transparent way.This article proposes a transparent machine learning framework for predicting delayed follow-up after emergency department visits. The objective is to support patient-specific discharge planning by identifying both the likelihood of delay and the most actionable contributing barriers. The proposed model would combine structured emergency department and scheduling data with natural language processing features extracted from discharge instructions. A gradient-boosted tree model with SHAP-based explanations would provide patient-level and population-level interpretability. Conceptually, the model would identify patients at elevated risk of delayed follow-up and attribute that risk to factors such as unclear instructions, limited appointment availability, absent portal engagement, transportation barriers, or higher visit complexity. These explanations would be intended to guide targeted interventions rather than replace clinical judgment. A transparent model for delayed follow-up prediction could enable precision transitional care after emergency department discharge. By aligning predictions with actionable explanations, care teams could direct limited resources toward the specific barrier most likely to prevent timely follow-up.

Administrative tasks surrounding a clinical encounter include documentation, coding, billing, insurance verification, prior authorization, and care coordination. These tasks are unevenly distributed across encounters and can consume substantial clinical and operational capacity. Health systems often detect administrative overload only after coding backlogs, payer denials, unanswered messages, or staff overtime have already emerged. The absence of an encounter-level prediction tool limits the ability of practices to intervene before administrative work accumulates. This article proposes a machine learning model that predicts whether an encounter is likely to become a high-administrative-burden event. The model uses documentation complexity, billing requirements, insurance rules, care coordination needs, and provider workload indicators as core predictors. A gradient-boosted classification framework is conceptually specified using historical encounter, billing, scheduling, payer, and workload data. The model would generate an encounter-level burden risk score and provide interpretable feature-domain contributions to support operational decisions. Conceptually, the model could identify encounters likely to require additional coding review, prior authorization follow-up, payer documentation, or multidisciplinary coordination. The resulting risk score would support proactive staffing, pre-visit review, and workflow routing. A predictive model for high administrative burden encounters could help shift healthcare administration from reactive queue management to anticipatory operational planning. Such a model may support revenue integrity, reduce avoidable rework, and lessen administrative strain on clinicians and staff.