Chapter One — 1.1 Background to the Study

The rapid evolution of Generative AI has transformed the way organizations design, deploy, and manage hospital appointment scheduling systems. As institutions seek to modernize legacy processes, Generative AI offers new opportunities to improve service delivery, reduce manual overhead, and respond more effectively to user needs.

Despite this potential, many existing hospital appointment scheduling systems were not originally designed with generative AI in mind, resulting in persistent gaps in fraud detection accuracy that limit their overall effectiveness. This study examines how Generative AI can be applied to help close that gap.

1.2 Statement of the Problem

Existing approaches to fraud detection accuracy within hospital appointment scheduling systems remain largely reactive and fragmented, with little systematic use of generative AI despite its demonstrated value elsewhere. This study addresses the resulting gap by designing and evaluating a solution built specifically around generative AI.

1.3 Objectives of the Study

1. To design and implement a generative AI-based approach to improving fraud detection accuracy in hospital appointment scheduling systems.
2. To evaluate the effectiveness of Generative AI in enhancing fraud detection accuracy within hospital appointment scheduling systems.
3. To identify the key requirements and constraints relevant to deploying generative AI in this context.
4. To assess user and stakeholder perception of the resulting system.

1.4 Research Questions

1. How can generative AI be applied to improve fraud detection accuracy in hospital appointment scheduling systems?
2. How effective is Generative AI at enhancing fraud detection accuracy within hospital appointment scheduling systems?
3. What requirements and constraints are relevant to deploying generative AI in this context?
4. How do users and stakeholders perceive the resulting system?

1.5 Significance of the Study

Beyond its immediate technical contribution, this study offers value to organizations evaluating whether to invest in generative AI for their own hospital appointment scheduling systems, and contributes to the broader literature on applied software technology / IT by documenting a concrete implementation and evaluation case.

1.6 Scope of the Study

The study is limited to the design, implementation, and evaluation of a generative AI-based approach to improving fraud detection accuracy within hospital appointment scheduling systems. Reflecting its clearly defined scope, it does not extend to a full commercial rollout or long-term post-implementation review beyond the study period.