An Explainable AI-Driven Healthcare Business Analytics Framework for Intelligent Hospital Management Systems
Keywords:
AI in healthcare, Clinical decision support systems, Data-driven healthcare, Explainable artificial intelligence (XAI), Healthcare business analytics, Healthcare informatics, Intelligent hospital management systemsAbstract
The rapid digital transformation of healthcare systems, which has generated an unprecedented volume of clinical, operational, and financial data, has simultaneously exposed significant limitations in conventional hospital management systems, particularly in relation to transparency, interpretability, and stakeholder trust in artificial intelligence (AI)-driven decision-making processes. Although AI technologies have demonstrated substantial potential in optimizing healthcare operations, their widespread adoption within hospital management environments continues to be hindered because many predictive models operate as opaque “black-box” systems whose decision-making mechanisms remain insufficiently explainable to administrators, clinicians, and regulatory authorities. To address these challenges, this study proposes a novel Explainable AI-driven Healthcare Business Analytics Framework (XAI-HBAF), which is specifically designed to facilitate intelligent, transparent, and data-driven decision-making within hospital management systems. The proposed framework integrates machine learning algorithms, XAI techniques, and advanced business analytics methodologies in a unified architecture that supports predictive, prescriptive, and descriptive analytics while simultaneously ensuring interpretability and accountability for diverse stakeholders. Furthermore, a hybrid analytical architecture, which combines ensemble learning models with SHAP (Shapley Additive Explanations), has been developed so that both prediction accuracy and model transparency can be significantly enhanced across critical hospital management functions, including patient flow optimization, resource allocation, and financial forecasting. Experimental evaluation, which was conducted using simulated hospital datasets, demonstrates that the proposed framework not only improves prediction accuracy by approximately 18–25% but also reduces operational inefficiencies by nearly 15% when compared with traditional analytics models. In addition, the incorporation of an explainability layer strengthens stakeholder confidence, enhances decision transparency, and supports compliance with evolving healthcare regulations and ethical AI standards. Consequently, the findings of this study suggest that XAI-driven healthcare analytics frameworks can substantially transform conventional hospital management systems into intelligent, adaptive, and trustworthy data-driven ecosystems capable of supporting sustainable and efficient healthcare delivery.
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