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In the evolving landscape of healthcare systems, predicting and managing patient length-of-stay (LOS) remains pivotal for operational efficiency. Yet, traditional models often overlook the interplay of real-time constraints and explainability. This conceptual manuscript introduces the constrained flow dynamics integrator (CFDI), a semi-mechanistic framework designed to model patient flow under operational constraints while prioritizing interpretability. Grounded in theoretical architectures from clinical AI and healthcare analytics, the CFDI integrates modular layers for constraint mapping, mechanistic simulation, and explainable inference, enabling hypothetical orchestration of patient trajectories without empirical data. By incorporating feedback topologies that simulate governance and interoperability, the framework addresses challenges in electronic health record (EHR) ecosystems and decision support pipelines. Conceptual formulas capture risk propagation across constrained environments and decision confidence in flow modeling, offering interpretive insights into resource allocation and monitoring burdens. This work synthesizes recent literature on AI governance and workflow integration, proposing a unique system for theoretical patient flow optimization. Implications extend to enhanced infrastructural resilience in healthcare settings, fostering transparent analytics amid operational pressures. Ultimately, the CFDI advances conceptual paradigms for explainable modeling, bridging gaps in constrained healthcare intelligence without relying on performance metrics or simulations.

Care pathways represent the temporal sequences of clinical events that define real-world patient journeys within complex healthcare systems. Recent advances in artificial intelligence have enabled the analysis of these pathways through sequence analytics, uncovering latent patterns beyond traditional guideline-based approaches. This narrative review synthesizes literature to examine three pillars of AI-enabled care pathway analytics: clustering methods that group similar patient trajectories, deviation detection techniques that identify meaningful variations from expected flows, and interpretability frameworks that support transparency and clinician trust.Drawing on process mining, sequence analysis, and explainable AI, the review highlights how electronic health record data can be transformed into actionable insights for clinical decision-making. Clustering approaches reveal hidden patient subgroups across domains such as oncology, cardiology, mental health, and critical care. Deviation detection methods expose bottlenecks, workarounds, and non-adherence associated with adverse outcomes and inefficiencies. Interpretability frameworks link algorithmic outputs to clinical logic, improving trust and adoption in healthcare settings.Cross-study evidence shows that while clustering and deviation detection methods have advanced significantly, their integration with interpretability remains limited, constraining large-scale implementation. The review proposes an integrative systems perspective that positions care pathway sequence analytics as a foundational component of AI-enabled healthcare infrastructure, encompassing data pipelines, model inference, intervention orchestration, and governance. Overall, AI-driven pathway analytics offers the potential to move healthcare from reactive, guideline-based care toward proactive, personalized, and continuously learning systems.

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.

Appointment cancellations undermine clinic efficiency, disrupt continuity of care, and reduce access for patients waiting for limited appointment slots. Many cancellations may be preventable when risk is recognized early enough for staff to intervene with reminders, rescheduling support, transportation assistance, or telemedicine conversion. Existing appointment-risk models often emphasize historical attendance and demographic information while underusing scheduling notes, patient communication history, weather conditions, and transportation barriers. They also frequently provide risk scores without patient-specific explanations that staff can translate into meaningful outreach. This article proposes an explainable machine learning framework for predicting preventable appointment cancellations before the appointment occurs. The framework is designed to identify not only which appointments may be at risk, but also why the cancellation risk is elevated. The proposed framework uses a gradient-boosted classification model trained on structured scheduling variables, prior attendance behavior, communication history, weather-linked features, transportation indicators, and natural language processing outputs from scheduling notes. SHAP-based explanation layers would decompose each prediction into interpretable drivers that can be reviewed by scheduling staff, clinic managers, and governance teams. Conceptually, the framework would output a cancellation risk score together with a natural-language explanation of the dominant drivers. These outputs could support targeted interventions such as reminder escalation, proactive rescheduling, transportation support, or conversion to a virtual visit when appropriate. An explainable framework for preventable appointment cancellation prediction could shift patient access management from reactive backfilling toward proactive retention. By combining heterogeneous access signals with transparent attribution, clinics could better align outreach resources with patient-specific barriers.