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The integration of artificial intelligence (AI) into healthcare has enhanced data-driven decision-making, but missing data remains a major barrier to reliable model performance. This narrative review synthesizes literature on missing data in clinical machine learning, focusing on modeling decisions, common pitfalls, and emerging reporting standards within AI-enabled healthcare systems.Missing data in healthcare arises from sources such as electronic health records (EHRs), wearable devices, and clinical trials, and may follow mechanisms including missing completely at random (MCAR), missing at random (MAR), or missing not at random (MNAR). Addressing these gaps requires appropriate imputation strategies, from statistical methods like multiple imputation to advanced deep learning approaches such as generative adversarial networks (GANs), each carrying implications for bias and model generalizability.This review highlights key challenges, including underreporting of missingness, insufficient sensitivity analyses, and neglect of imputation uncertainty. It also examines evolving reporting standards that emphasize transparency in missing data handling. By synthesizing cross-study evidence, the review proposes a systems-level framework for integrating missing data management into AI governance, supporting more reliable, transparent, and equitable healthcare analytics.

Hospital workflow analytics has become central to improving throughput, reducing operational cost, and strengthening patient experience. Artificial intelligence offers predictive capabilities for patient flow, staffing, resource use, and delay anticipation. This systematic review examined machine learning models applied to patient flow, staff scheduling, resource utilisation, and operational delay prediction in hospital settings. The review focused on model types, operational endpoints, data sources, validation methods, and implementation maturity. A PRISMA 2020-aligned search strategy was designed for PubMed, Scopus, IEEE Xplore, and Web of Science. Screening, extraction, risk-of-bias appraisal, and narrative synthesis were structured around hospital operations rather than clinical diagnosis. The literature was dominated by retrospective, single-centre studies focused on patient flow, especially length-of-stay, admission, discharge, and bed-use prediction. Staffing, resource utilisation, and operational delay prediction were less frequently studied, and prospective deployment remained uncommon. Machine learning for hospital operations is maturing technically but remains fragmented across isolated workflow domains. Integration across patient flow, staffing, resource utilisation, and delay management requires stronger prospective evaluation.

Hospital length-of-stay is a central operational metric for inpatient capacity planning, discharge coordination, and resource allocation. Accurate prediction remains difficult because patient trajectories are heterogeneous, nonlinear, and shaped by evolving clinical events during admission. Traditional statistical models often have limited flexibility for high-dimensional and sequential electronic health record data. Across the literature, there is no settled consensus regarding optimal model architecture, feature representation, validation design, or clinical implementation strategy. This systematic review synthesizes machine learning approaches for hospital length-of-stay prediction published from 2017 to 2022. It focuses on EHR feature types, model architectures, validation methods, interpretability strategies, and reported operational outcomes. A structured review of peer-reviewed literature was conducted using targeted search strings related to machine learning, deep learning, electronic health records, discharge prediction, and hospital length-of-stay. The review included studies across emergency, inpatient, surgical, pediatric, cardiovascular, and intensive care settings. The literature suggests that gradient boosting, random forest, ensemble learning, and recurrent neural networks are common approaches for LOS prediction. However, external validation remains uncommon, prediction horizons vary widely, and operational implementation outcomes are reported less consistently than model development results. Future research should prioritize external validation, prospective implementation studies, standardized outcome definitions, and transparent reporting of workflow barriers. Shared benchmarking datasets and multi-center validation consortia would strengthen comparability across LOS prediction studies.

Machine learning is increasingly used in hospital operations, revenue cycle management, and quality monitoring. These administrative applications require transparency because their outputs can influence access, resource allocation, financial decisions, and accountability. This systematic review examined explainable machine learning methods applied to operational decision support, revenue cycle analytics, and quality monitoring in healthcare administration. The review focused on the type, depth, and use of transparency methods rather than predictive performance. A PRISMA 2020–compliant search strategy was applied to PubMed, Scopus, IEEE Xplore, and Web of Science for studies published between 2017 and 2022. Screening, extraction, and narrative synthesis focused on administrative domain, model type, explanation method, stakeholder use, and risk of bias. SHAP and LIME were the most frequently discussed post-hoc explanation approaches, while feature importance, partial dependence, rule-based models, and attention mechanisms appeared in smaller subsets of the literature. Operational decision support showed the strongest explainability uptake, whereas revenue cycle analytics and administrative quality monitoring remained less developed. Explainability in healthcare administration remains uneven and often superficial. The largest gap is not the availability of explanation tools, but the limited evidence that explanations improve managerial decisions, accountability, fairness, or auditability.

Efficient hospital resource allocation is a persistent operational challenge because demand for beds, staff, equipment, diagnostics, and patient movement changes rapidly. Predictive analytics offers a way to anticipate demand and support more proactive operational decisions. This systematic review synthesised machine learning models applied to hospital resource allocation from 2017 to 2023. The review focused on bed management, staffing demand, equipment use, diagnostic capacity, and patient throughput. A PRISMA 2020-compliant review process was used, including structured searches of PubMed, Scopus, IEEE Xplore, and Web of Science. Screening, extraction, risk-of-bias assessment, and narrative synthesis were conducted to compare model targets, data sources, validation approaches, and implementation maturity. The evidence base was dominated by retrospective studies of patient throughput, emergency department admission prediction, bed demand, and length of stay. Staffing, equipment, and diagnostic capacity forecasting were less frequently represented, while integrated multi-resource command centre models remained uncommon. Machine learning for hospital resource allocation has become technically advanced, but operational translation remains uneven. Most models remained retrospective or locally validated, with limited evidence of prospective deployment, workflow integration, or measurable operational impact.

Revenue cycle inefficiencies, including claim denials, coding errors, delayed reimbursement, and prior authorization workload, impose substantial administrative and financial burdens on healthcare organizations. Machine learning has been proposed as a decision-support approach for improving prediction, automation, and workflow prioritization in these areas. This systematic review examined peer-reviewed and closely related scholarly literature from 2017 to 2023 on machine learning for healthcare revenue cycle analytics. The review focused on claim denial prediction, coding automation, payment delay forecasting, and prior authorization support. A PRISMA 2020-compliant review process was used, including structured database searching, dual screening, and domain-based narrative synthesis. Risk of bias was assessed using criteria adapted from PROBAST-AI, with attention to temporal validation, data leakage, and implementation relevance. The literature showed the greatest maturity in automated clinical coding and emerging but narrower evidence for claim denial prediction. Evidence for payment delay forecasting and prior authorization support was more limited, with few studies describing prospective implementation or measured operational impact. Machine learning shows promise for improving revenue cycle decision support, but most evidence remains retrospective and technically oriented. Deployment is constrained by data fragmentation, explainability requirements, workflow integration, and regulatory caution.

Hospital patient flow depends on timely transfer decisions, accurate discharge planning, and reliable post-discharge coordination. Delays in placement, premature discharge, missed follow-up, and unrecognized post-discharge risk can increase avoidable utilization and compromise continuity of care. This systematic review examined machine learning models for predicting placement delay, discharge readiness, follow-up completion, and post-discharge risk from 2017 to 2024. The objective was to synthesize model purposes, data sources, validation practices, implementation maturity, and practical relevance for care transitions. A PRISMA 2020-compliant systematic review was conducted using PubMed, Scopus, IEEE Xplore, and Web of Science. Dual screening, structured data extraction, and narrative synthesis were used because heterogeneity in model objectives, predictors, settings, and outcomes prevented meta-analysis. The evidence base was largest for post-discharge readmission risk and discharge readiness prediction, while placement delay and follow-up completion received less focused attention. Prospective validation, workflow integration, and outcome-based implementation studies were uncommon across all four domains. Machine learning offers promising tools to support proactive discharge planning and transitional care, but the evidence remains dominated by retrospective model development. Translation into routine practice requires prospective testing, interpretability, equity assessment, and integration into multidisciplinary workflows.

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.