Predicting counseling behavioral propensity based on temporal return visits patterns and current perceived intensity with chronic conditions management

Abstract

The healthcare demand for online medical counseling has increased. Management of chronic conditions through Internet hospitals and online platforms has shifted from cure-centered approaches to service-oriented counseling. Previous studies have given less attention to the combined behavioral and perceived determinants that influence subsequent counseling, particularly in terms of types and timing. Our research identifies key determinants including sequential intertemporal behavioral patterns of return visits, perceived current counseling intensity measured by perceived usefulness and emotional attitudes, as well as diagnosis-oriented clustering enhancements suitable for chronic conditions specialties. The Diagnosis-specific Interval-scaled Perception-sensitive (DIPs) framework integrates nearly 380 thousand real dialogues from Chinese electronic healthcare records (CEHRs) and auxiliary information. Performance evaluations using the receiver operating characteristic (ROC) curve and the precision-recall curve (PRC) yield high scores of 0.95 Area Under the ROC Curve (AUROC) and 0.72 Area Under the Precision-Recall Curve (AUPRC), which is significant in unbalanced multi-classification tasks offering a solution to chronic online counseling behavioral propensity estimation and widespread adoption in real-world settings. DIPs framework's credible quantitative interpretability provides insights into prioritizing behavioral and perceptual impacts over computational features in counseling propensity predictions. Platforms and physicians can facilitate targeted interventions that align patients’ expectations with the sustainable delivery of on-demand services in chronic conditions management.