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Patient no-shows in outpatient clinics (5%–30% across specialties) disrupt scheduling efficiency, increase wait times, and strain healthcare resources. To address this, healthcare systems are increasingly applying machine learning (ML) for predictive scheduling support. This systematic review synthesizes ML approaches for predicting outpatient no-shows, focusing on model types, feature usage, and reported operational deployment outcomes, with emphasis on translation into clinical scheduling practice. A PRISMA-compliant search of PubMed, Embase, IEEE Xplore, Scopus, and Web of Science identified studies using ML for no-show prediction in outpatient settings. Data on models, features, performance, and implementation were extracted. Risk of bias was assessed using an adapted PROBAST tool. Thirty-two studies were included. Logistic regression, random forest, and XGBoost were the most commonly used models. Historical attendance data was the dominant predictive feature. Fewer than 20% of studies reported real-world implementation, and reported intervention outcomes (e.g., overbooking, reminders) were inconsistent. While ML models show strong predictive performance, real-world deployment and evidence of operational impact remain limited. This gap highlights the need to prioritize implementation-focused research to translate predictive accuracy into measurable improvements in clinic efficiency and access.

Diagnostic test overuse in outpatient settings contributes to wasteful spending and may expose patients to unnecessary downstream testing, anxiety, and treatment cascades. Common examples include low-value imaging, repetitive laboratory testing, and routine preoperative or annual tests without clear clinical indication. Existing approaches often rely on manual review, retrospective measurement, or rigid rule-based filters. These approaches may miss context-sensitive overuse shaped by visit type, clinician habit, patient complexity, and recent test history. This manuscript proposes an interpretable random forest model that predicts whether a diagnostic test order could represent overuse. The model integrates visit-level, physician-level, patient-level, prior-result, and guideline-based appropriateness features. The proposed model would use historical outpatient orders labelled through appropriateness criteria or expert review. SHAP-based explanations would be used to make each prediction interpretable to clinicians and quality leaders. Conceptually, the model would estimate an overuse probability and identify the relative contribution of physician ordering history, patient complexity, prior results, visit type, and guideline indicators. These explanations would support targeted feedback rather than opaque surveillance. An interpretable random forest model could function as a personalised overuse screening tool within clinical decision support. Its value would depend on transparent explanations, careful guideline alignment, and thoughtful integration into outpatient workflows.