Background: Healthcare supply chains have long struggled with fragmented data, inconsistent demand signals, and reactive replenishment practices. The COVID-19 pandemic exposed these vulnerabilities on a global scale, producing severe shortages of personal protective equipment, medications, and critical medical devices that compromised both operational continuity and patient safety.

Objective: This narrative review synthesizes the peer-reviewed and grey literature on predictive analytics as applied to medical inventory management, with particular attention to its implications for healthcare quality, cost containment, and patient safety.

Methods: A structured but non-systematic search of academic databases, publisher platforms, and institutional reports was conducted to identify studies, case reports, and reviews addressing predictive and prescriptive analytics, machine learning, and related digital technologies (radio-frequency identification, the Internet of Medical Things, and digital twins) in healthcare inventory and supply chain contexts. Findings were synthesized narratively around a proposed conceptual framework linking predictive analytics to healthcare quality and cost outcomes.

Results: Across the literature, predictive analytics is consistently associated with improved demand forecasting accuracy, reduced stockouts and overstock, shorter procedure delays, and measurable cost savings, with individual case studies reporting inventory cost reductions in the range of roughly one-quarter to nearly one-half. Radio-frequency identification and Internet of Things-enabled tracking further improve real-time visibility, while digital twins and vendor-managed inventory arrangements show emerging but less mature evidence. Common barriers include data quality limitations, interoperability constraints between enterprise resource planning and electronic health record systems, workforce readiness, cybersecurity exposure, and organizational resistance to change.

Conclusion: Predictive analytics offers a credible, evidence-supported pathway toward more resilient, cost-effective, and safety-oriented medical inventory management, but realizing its benefits at scale requires deliberate investment in data governance, system interoperability, and change management alongside the underlying algorithms.

Keywords: predictive analytics, healthcare supply chain, medical inventory management, machine learning, patient safety, cost reduction, healthcare quality, digital twin, Internet of Medical Things.