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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.

Healthcare operations are constrained by demand volatility, resource scarcity, staffing pressures, and interdependent patient pathways. Artificial intelligence and predictive analytics offer a way to anticipate operational stress before it becomes visible in queues, bed shortages, overtime, or delayed care. This systematic review examines predictive analytics models applied to hospital staffing, scheduling, bed capacity, patient flow, and service demand forecasting from 2017 to 2022. The objective is to synthesize model types, data sources, operational targets, validation approaches, and implementation maturity across these domains. A PRISMA 2020–compliant review design was used to guide database searching, screening, eligibility assessment, extraction, and synthesis. Searches covered PubMed, Scopus, IEEE Xplore, and Web of Science, with narrative synthesis grouped by operational domain and risk of bias considered using an operationally adapted PROBAST-AI lens. The evidence base was dominated by retrospective, single-centre studies demonstrating the technical feasibility of predictive analytics for bed demand, emergency department arrivals, admission prediction, discharge prediction, and length-of-stay estimation. Staffing and scheduling studies were less frequent, and prospective implementation in real operational workflows remained uncommon. Predictive analytics for healthcare operations management is technically mature but practically under-deployed. The central challenge is translating forecasts into staffing, scheduling, bed-management, and command-centre decisions that measurably improve operational performance.

Patient access to timely, appropriate care remains a persistent challenge for health systems, affecting clinical continuity, patient experience, and operational performance. Artificial intelligence has been proposed as a means of automating and optimizing navigation functions from scheduling to follow-up coordination. This systematic review examined artificial intelligence applications for care navigation and patient access across scheduling optimization, referral management, eligibility screening, digital front-door tools, and follow-up coordination. The review also assessed implementation maturity, evaluation approaches, and equity-related reporting. A PRISMA 2020-compliant search was conducted across PubMed, Scopus, IEEE Xplore, and Web of Science for publications from 2017 through 2025. Dual screening, structured data extraction, risk-of-bias assessment, and narrative synthesis were used to characterize the evidence. The literature was concentrated in scheduling optimization, particularly no-show prediction and operational appointment management, and in digital front-door tools such as symptom checkers and triage chatbots. Referral and eligibility applications were emerging, while follow-up coordination models often overlapped with readmission and care-transition prediction. Few studies reported prospective implementation, comparative deployment outcomes, or equity impacts. Artificial intelligence-driven patient access tools appear technically robust for isolated tasks, but evidence that they improve integrated, end-to-end navigation remains limited. The field requires stronger prospective evaluation, equity assessment, and implementation reporting.