American Journal of Advanced Multidisciplinary Research and Innovation

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Artificial Intelligence-Driven Decision Support Systems for Sustainable Healthcare Management: A Multidisciplinary Perspective

Author(s) Fei-Fei Li
Country United States
Abstract Artificial Intelligence (AI) has emerged as a transformative technology in modern healthcare by enhancing clinical decision-making, improving operational efficiency, and supporting sustainable healthcare management. Healthcare systems worldwide face increasing challenges associated with ageing populations, chronic disease prevalence, rising healthcare costs, workforce shortages, and unequal access to quality medical services. Artificial Intelligence-Driven Decision Support Systems (AI-DSS) provide intelligent, data-driven recommendations that assist healthcare professionals in diagnosis, treatment planning, resource allocation, disease surveillance, predictive analytics, and hospital management. These systems integrate machine learning, deep learning, natural language processing (NLP), computer vision, Internet of Medical Things (IoMT), electronic health records (EHRs), Digital Twins, and cloud-edge computing to facilitate evidence-based decision-making while improving patient outcomes and organisational sustainability.
This study investigates Artificial Intelligence-driven Decision Support Systems for sustainable healthcare management from a multidisciplinary perspective using a qualitative and analytical research methodology based on secondary data collected from peer-reviewed journals, healthcare reports, international guidelines, and case studies. The study examines AI algorithms, predictive analytics, explainable artificial intelligence (XAI), precision medicine, intelligent resource management, telemedicine, and public health decision support. Furthermore, it evaluates the impact of AI-DSS on clinical accuracy, healthcare accessibility, operational efficiency, patient safety, environmental sustainability, and healthcare governance while identifying implementation challenges and future research opportunities.
The findings indicate that AI-driven decision support systems significantly improve diagnostic accuracy, personalised treatment planning, predictive disease modelling, hospital resource optimisation, clinical workflow efficiency, and healthcare sustainability. The integration of Explainable AI, Digital Twins, Internet of Medical Things (IoMT), and federated learning further enhances transparency, interoperability, privacy protection, and trust in intelligent healthcare systems. However, challenges including data privacy, algorithmic bias, interoperability, cybersecurity, regulatory compliance, ethical governance, and workforce readiness continue to affect widespread adoption.
The study concludes that Artificial Intelligence-driven Decision Support Systems represent a fundamental technological advancement for sustainable healthcare management and will play a central role in the transition toward intelligent, patient-centred, resilient, and equitable healthcare systems.
Keywords Artificial Intelligence, Clinical Decision Support Systems, Sustainable Healthcare, Healthcare Management, Explainable AI, Predictive Analytics, Precision Medicine, Electronic Health Records, Digital Health.
Field Engineering
Published In Volume 1, Issue 1, January-February 2019
Published On 2019-01-04
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