Chapter One — 1.1 Background to the Study

The rapid evolution of Generative AI has transformed the way organizations design, deploy, and manage electronic health records. 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 electronic health records were not originally designed with generative AI in mind, resulting in persistent gaps in data security 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

Current electronic health records in many organizations struggle with inadequate data security, often relying on manual processes or outdated architectures that were not designed for today's operating environment. Without a structured approach to integrating generative AI, these limitations are likely to persist, exposing organizations to inefficiency, risk, and a poor user experience. This study is motivated by the need to design and evaluate a generative AI-based approach to addressing this problem.

1.3 Objectives of the Study

1. To design and implement a generative AI-based approach to improving data security in electronic health records.
2. To evaluate the effectiveness of Generative AI in enhancing data security within electronic health records.
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 data security in electronic health records?
2. How effective is Generative AI at enhancing data security within electronic health records?
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 electronic health records, 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 data security within electronic health records. Reflecting its clearly defined scope, it does not extend to a full commercial rollout or long-term post-implementation review beyond the study period.