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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.

Outpatient clinics routinely overbook to compensate for patient no-shows, but poorly calibrated overbooking can create provider overtime, patient wait time, and staff burnout. The operational challenge is to preserve access without overwhelming clinical capacity. Current overbooking rules often rely on static session-level averages. These rules ignore the dynamic risk profile of the specific patient being added to the schedule. This article develops a predictive analytics model that estimates overbooking risk for each proposed additional appointment. The model uses patient-specific features together with provider, schedule, seasonal, and communication context. The proposed model would use gradient-boosted classification or regression trained on historical appointment data. Its output would be a risk score reflecting the likelihood of excessive wait time, overtime, or queue formation for the session. Conceptually, the model would flag situations in which adding a particular patient to a dense session creates high operational risk. It would also identify lower-risk overbooking opportunities when the schedule has sufficient flexibility. The model could enable precision overbooking in outpatient clinics. It would support access and efficiency while reducing the negative consequences of both no-shows and excessive overbooking.