A developing Data-Driven 'Nudge' Strategies to Enhance Preventive Care Compliance and Reduce Systemic Expenditure in Public Health Clinics

Abstract

Preventive care non-compliance remains a persistent challenge in public health clinics, contributing to avoidable

disease progression and escalating systemic expenditures. This research paper develops a data-driven behavioral

intervention framework, grounded in nudge theory, to enhance patient adherence to preventive screenings and

vaccinations while reducing overall costs. The problem is defined by the gap between evidence-based preventive

guidelines and actual patient behavior, often exacerbated by cognitive biases and resource constraints. The purpose is

to 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 a

quasi-experimental pilot intervention (n=450 patients). Predictive business analytics, following methods discussed by

Hossain et al. (2023), were employed to segment patients based on predicted non-compliance risk. Nudge

strategiesincluding opt-out framing, loss-framed messaging, and social comparison feedback—were deployed via a

secure patient portal. Key findings indicate that data-driven nudges increased preventive screening completion by

28.6% (p<0.01) and reduced per-patient systemic expenditure on downstream acute care by 17.4% over six months.

The conclusion implies that integrating behavioral economics with routine clinical data infrastructures offers a

scalable, low-cost mechanism for improving public health outcomes and financial sustainability. This study

contributes empirical evidence for policy makers and clinic administrators seeking non-regulatory interventions to

optimize preventive care delivery.