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Case management programs are intended to reduce avoidable utilization and improve coordination for patients with complex medical, social, and engagement needs. Because case management capacity is limited, health systems need prioritization tools that are both clinically sensible and transparent. Existing referral methods often rely on clinician judgment, simple utilization thresholds, or proprietary risk scores that provide limited explanation. These approaches may overlook social needs, missed appointments, and care gaps that shape patient complexity and influence whether an intervention is feasible. This article proposes an explainable machine learning model that stratifies patients by risk of future high utilization and provides patient-specific reasoning. The model is designed around prior utilization, chronic disease burden, social needs documentation, missed appointments, and care gap indicators. The conceptual architecture uses a gradient-boosted classification model with a SHAP-based post-hoc explanation layer. The model would output both a risk score and a ranked list of contributing factors for each patient considered for case management referral. Conceptually, the model would identify patients who may benefit from case management and explain why each patient was prioritized. These explanations could help case managers tailor outreach, match patients to intervention pathways, and distinguish medical complexity from social instability or disengagement. An explainable risk stratification model could turn a blind referral process into a transparent, clinically sensible prioritization workflow. Its value would depend on careful implementation, fairness monitoring, and alignment with real case manager decision-making.

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.

Prior authorization delays impede timely patient care and contribute to administrative pressure across clinical and revenue cycle workflows. These delays can affect scheduling, medication access, procedural planning, and patient confidence in the care process. Current authorization management tools are largely reactive and often focus on tracking request status after submission. They rarely predict which requests are likely to experience approval delays or explain the operational, clinical, or payer-specific reasons behind those delays. This article proposes an interpretable machine learning model for predicting the likelihood of prior authorization approval delays. The model is designed to provide transparent, request-level explanations that can guide pre-submission correction and authorization preparation. The proposed framework uses a gradient-boosted tree model trained conceptually on historical authorization requests. Inputs include payer-specific rules, clinical documentation features, procedure type, medical necessity indicators, and historical approval timelines, with SHAP used to attribute predicted delay risk to individual features. Conceptually, the model would output a delay probability and an explanation of the dominant drivers of that prediction. These drivers could include incomplete documentation, mismatch with payer medical necessity criteria, procedure categories associated with additional review, or payer-procedure combinations with historically slow turnaround. An interpretable prior authorization delay model could support earlier correction of incomplete requests, reduce administrative waste, and improve patient access. By aligning predictive analytics with transparent explanations, the framework could make authorization preparation more proactive and accountable.