Adherence Risk Stratification in Physiatry: A Multivariate Analysis of Factors in Community-Based Care Using Algorithmic Modeling Techniques

Missed appointments represent a double-edged sword in community health settings. Policies designed to retain patients and ensure continuity of care for vulnerable populations often mean that discharging patients is rare, even in cases of frequent no-shows. However, this retention strains healthcare resources, disrupts workflows, and exacerbates inequities in access to care. In physiatry (PM&R), where rehabilitation outcomes depend on consistent patient engagement, missed visits further hinder progress, delaying recovery and diminishing quality of life. Addressing appointment adherence in these settings is paramount for equitable and efficient care delivery. This study evaluated appointment adherence using EPIC-derived general risk scores (demographics, clinical history, and other individual-level factors) and preventative gap scores (compliance with recommended preventative care guidelines). To add granularity, demographic variables such as age, sex, and race/ethnicity--Social Drivers of Health (SDOH) factors embedded within these risk scores--were further analyzed as an additional layer to identify structural and systemic barriers influencing patient engagement. A Residual Deep Neural Network (RDNN) was developed, achieving an AUC-ROC of 0.997, recall of 0.988, F1-score of 0.987, and accuracy of 0.980. A Deep Neural Network with Attention (DNNA) was introduced for interpretability, offering opportunities to refine and extend RDNNs predictive performance. It demonstrated a 5.7x improvement over a clamped baseline for no-show risk prediction. These findings emphasize the strengths of combining RDNNs robust predictive capabilities with DNNAs ability to model nuanced relationships. Together, they provide a pathway to optimize appointment adherence and enhance equitable care delivery in community health and PM&R settings.