Patient Billing and Collections Optimization Through Behavioral Segmentation and Data-Driven Outreach Strategies

Abstract

This research paper presents a novel framework for optimizing patient billing and collections processes in healthcare organizations through advanced behavioral segmentation and data-driven outreach strategies. We develop a mathematical model that integrates multidimensional patient financial behavior indicators with temporal payment patterns to predict future payment likelihood with unprecedented accuracy. Our approach employs non-parametric Bayesian methods and deep neural networks to identify latent behavioral clusters and dynamically assign patients to optimal communication channels, timing intervals, and message framing. Experimental implementation across three diverse healthcare systems demonstrates statistically significant improvements in key performance metrics: 31.4\% reduction in days in accounts receivable, 27.8\% increase in collection rate, and 19.3\% decrease in administrative costs associated with collection activities. The return on investment calculation indicates a 3.42x multiplier effect when accounting for both direct collection improvements and operational cost reductions. This research contributes to the nascent field of behavioral economics in healthcare revenue cycle management and establishes a quantitative foundation for further optimization of patient financial engagement strategies.