Data-Enabled Service-Line Rationalization Frameworks to Enhance Health-System Profitability and Competitive Market Position

Abstract

In an era of increasing financial pressure and market competition, healthcare systems must make strategic decisions about which service lines to maintain, expand, or consolidate. Traditional approaches to service-line planning often rely on fragmented data and heuristic methods that may overlook key interdependencies. This research presents a comprehensive framework for service-line rationalization in healthcare systems using advanced data analytics and mathematical modeling techniques. We develop a novel approach that integrates financial performance metrics, market demand analysis, competitive positioning, and operational efficiency measures into a unified decision-support system. The framework employs stochastic optimization models to account for uncertainty in patient volumes, reimbursement rates, and resource utilization. Through implementation of multidimensional scaling and hierarchical clustering algorithms, our methodology identifies strategic service-line portfolio configurations that maximize system-wide contribution margins while maintaining essential healthcare access. A game-theoretic market equilibrium model further enhances the framework by incorporating competitive responses to service-line changes. Mathematical validation using Monte Carlo simulations demonstrates the framework's robustness under various market conditions. The computational experiments reveal potential profitability improvements of 8–13\% with simultaneous enhancements in market coverage metrics. This approach provides healthcare executives with quantitative tools to navigate the complex interplay between financial sustainability and market position, enabling data-driven service-line rationalization decisions aligned with both institutional objectives and community healthcare needs.