Medical and health management: strategic research on AI-driven precision-oriented healthcare management

Abstract

Artificial intelligence (AI) is reshaping the architecture of modern health-services management, guiding clinical and administrative systems toward a precision paradigm that enhances therapeutic yield while economising scarce resources. This investigation develops and tests an integrative framework for AI-enabled precision management across heterogeneous organisational settings. By combining a PRISMA-based literature review, comparative case analysis of 17 healthcare institutions, quantitative scrutiny of 23 implementation projects, and 24-month longitudinal tracking, the study demonstrates that institutions equipped with coherent AI-governance arrangements achieve 37,4% higher clinical-outcome gains and 42,8% greater operational efficiency than peers deploying fragmented strategies. Enterprise-level fusion of predictive analytics with clinical-decision-support platforms elevates diagnostic accuracy (p 0.001) and cuts adverse-event incidence by 28,6%. Precision medication-management suites reduce prescribing errors by 31,2% and medication-related complications by 26,8%. Yet pervasive barriers – interoperability deficits, workforce-skill lacunae, and unresolved ethical-regulatory dilemmas – moderate these advantages. The proposed strategic framework reconciles technological innovation with human expertise and robust governance safeguards, hastening the transition toward continuously learning health systems capable of improving outcomes while optimising resource allocation.