Capital Equipment Financing and Risk Management in Healthcare Technology Procurement

Abstract

Aim. This article develops an integrative decision framework for capital equipment financing in healthcare technology procurement. It addresses three linked problems that existing treatments handle separately: fragmented measurement across finance, analytics, and risk functions; weak treatment of uncertainty in lifecycle cost and technology obsolescence; and a persistent separation between commercial financing decisions and clinical risk governance.
Materials and methods. A structured narrative synthesis draws on finance, analytics, technology, operations, governance, and sector-specific literature, compared thematically rather than treated as an undifferentiated bundle. Empirical, review, and conceptual sources are admissible because the objective is theory integration rather than pooled effect estimation.
Results. The resulting framework organizes evidence across clinical need, lifecycle cost, financing structure, technology obsolescence, supplier performance, and patient safety. It defines lifecycle clinical and financial value as the focal decision criterion and proposes a sequence of data definition, baseline construction, causal or comparative estimation, risk adjustment, scenario testing, governance review, and post-implementation learning. The analysis shows that isolated efficiency or revenue measures are insufficient because they omit implementation costs, tail losses, customer effects, and timing.
Conclusion. The article contributes a reusable architecture that links economic evaluation with accountable execution, closing the gap between capital appraisal tools built for generic assets and the clinical, regulatory, and supplier-dependent character of healthcare technology. It also specifies research propositions, measurement fields, validation tests, and raeporting practices suitable for empirical testing. No confidential company data are used and no causal claims are made about named employers. Decision quality improves when value, risk, behavior, and control effectiveness are evaluated within one auditable model.