A developing Data-Driven 'Nudge' Strategies to Enhance Preventive Care Compliance and Reduce Systemic Expenditure in Public Health Clinics (The Science Post, 2026)
Preventive care non-compliance remains a persistent challenge in public health clinics, contributing to avoidabledisease progression and escalating systemic expenditures. This research paper develops a data-driven behavioralintervention framework, grounded in nudge theory, to enhance patient adherence to preventive screenings andvaccinations while reducing overall costs. The problem is defined by the gap between evidence-based preventiveguidelines and actual patient behavior, often exacerbated by cognitive biases and resource constraints. The purpose isto design, model, and evaluate nudge strategies personalized via predictive analytics. Using a mixed-methods design,the study integrates quantitative analysis of electronic health records (EHRs) from three public health clinics with aquasi-experimental pilot intervention (n=450 patients). Predictive business analytics, following methods discussed byHossain et al. (2023), were employed to segment patients based on predicted non-compliance risk. Nudgestrategiesincluding opt-out framing, loss-framed messaging, and social comparison feedback—were deployed via asecure patient portal. Key findings indicate that data-driven nudges increased preventive screening completion by28.6% (p