Theses (Master of Business Administration in Healthcare Management)

The rapid advancement of Artificial Intelligence (AI) is transforming healthcare systems worldwide, offering significant potential to enhance efficiency, accuracy, and patient-centred care. Within nursing practice, AI applications, such as predictive analytics, clinical decision support, and automated documentation, can enhance clinical decision-making, reduce workload, and support professional development. However, integrating AI into nursing is not without challenges, as issues related to ethical considerations, job security, technological readiness, and the preservation of compassionate care must be addressed. This study aims to examine the opportunities and challenges of AI integration in nursing practice, with a focus on nurses' perceptions, preparedness, and behavioural intention towards adopting AI tools. A cross-sectional quantitative research design will be employed among registered nurses in Klang Valley to assess perceived usability, benefits, and challenges of AI adoption. The findings are expected to provide evidence-based insights into nurses’ readiness for AI adoption, inform ethical and practical strategies for integration, and guide policy recommendations for a sustainable digital transformation in healthcare. Ultimately, the research seeks to contribute to the advancement of nursing education, practice, and innovation while ensuring that the humanistic essence of care remains central in an AI-driven healthcare environment.

In Malaysia, a staggering 73.1% of consumers admit to non-compliance with prescribed medication, and over a third (33.9%) engage in the high-risk practice of medicine sharing. These figures reveal a critical breakdown in the nation's quality use of medicines, a problem now dangerously amplified by unregulated digital channels where health misinformation and illicit online pharmacies proliferate. This case study provides a comprehensive analysis of the Malaysian healthcare ecosystem to diagnose the drivers, risks, and operational realities of this escalating public health challenge. The findings reveal that self-medication is a highly prevalent practice, deeply embedded in the culture and driven by perceptions of convenience, safety, and the influence of past experience. The rapid adoption of digital health, particularly telehealth services for chronic disease management, offers a structured and beneficial avenue for patient care. However, this progress is dangerously undermined by the parallel growth of an unregulated online environment. During the recent pandemic, illicit vendors comprised up to 73.3% of search engine results for in-demand medicines, creating a significant patient safety crisis. The analysis further identifies critical operational gaps at both ends of the healthcare spectrum. Deficiencies in regulatory compliance among healthcare professionals, particularly in record-keeping and medicine labelling, are widespread. This is mirrored by severe deficits in public health literacy, evidenced by the high rates of medication non-adherence and sharing among consumers. To address these interconnected challenges, this report proposes a multi-pronged strategic approach centered on three core pillars: 1. Enhanced Public Health Literacy Launching a national campaign to educate consumers on responsible self-care practices and the inherent risks of inappropriate self-medication. 2. Strengthened Digital Governance Establishing a robust regulatory framework for online health platforms and certified pharmacies to combat misinformation and ensure patient safety. 3. Improved Operational Enforcement Enhancing regulatory oversight through proven effective measures and mandating continuing professional education to close compliance gaps. By implementing these recommendations, Malaysia can foster a safer and more effective self-care environment, empowering its citizens while safeguarding public health in an increasingly digital world.

Fraud, waste, and abuse (FWA) in healthcare represent a significant challenge to the sustainability and efficiency of healthcare financing systems worldwide. Within Third-Party Administrators (TPAs), which play a critical role in managing medical claims for self-insured employers, the reliance on predominantly manual claims processing exposes organizations to operational inefficiencies, financial leakage, and heightened fraud risks. This descriptive–explanatory case study examines the nature and extent of FWA in medical billing within a Malaysian healthcare TPA, analyses the limitations of existing manual claims checking processes, and explores the potential role of automation in strengthening fraud detection and risk management. Using a single-case study design, data were collected through semi-structured interviews with key operational personnel, document analysis of internal fraud management reports covering the period 2020–2025, and limited observation of claims workflows. The findings reveal recurring forms of FWA, including forging or altering of billing documents, phantom billing, misrepresentation of non-covered treatments, medical identity theft, and manipulation of diagnoses. Manual detection processes were found to be labour-intensive, reactive, prone to human error, and constrained by fragmented data systems and inconsistent rule application. Applying the Fraud Triangle Theory and internal control frameworks, the study explains how weaknesses in manual processes create opportunities for FWA. Comparative analysis indicates that automation through rule-based engines and Artificial Intelligence/Machine Learning (AI/ML) has strong potential to improve efficiency, enhance detection accuracy, reduce operational costs, and shift FWA management from a reactive to a proactive approach. The study concludes by offering managerial recommendations for the phased adoption of automation within Malaysian TPAs to strengthen governance, reduce financial losses, and improve the integrity of healthcare claims administration. Keywords: Fraud, Waste, and Abuse (FWA), Third-Party Administrators (TPA), Healthcare Claims Processing, Automation, Artificial Intelligence and Machine Learning