article · Asian Journal of Research in Computer Science
This research examines data governance challenges in artificial intelligence healthcare systems, drawing on a case study of Project Nightingale. Based on a survey of 843 healthcare service users, the investigation assessed public attitudes towards artificial intelligence, privacy concerns, trust in providers, and the influence of regulatory structures. The results demonstrate that greater awareness of technological initiatives boosts patient trust in healthcare providers, whereas privacy worries significantly undermine that trust. Furthermore, while confidence in regulatory frameworks strengthens trust in medical data handling, concerns surrounding regulatory constraints and weak governance present substantial hurdles to artificial intelligence adoption. To resolve these tensions, the findings emphasise the necessity of transparent operations, strong governance rules, and flexible regulatory mechanisms developed through multi-stakeholder partnerships to protect patient welfare while encouraging technological uptake.
As artificial intelligence becomes central to medical services, maintaining patient trust is vital. This research demonstrates that technological deployment cannot succeed without clear communication and reliable safeguards. Patients are willing to trust innovation when they understand it and see effective oversight, but privacy fears and unclear governance can quickly stall the adoption of valuable tools across healthcare systems.
The abstract presents survey-based empirical research rather than a commercial product or technical tool. It informs policy design, compliance frameworks, and public engagement approaches for healthcare organisations and digital health developers deploying artificial intelligence. The insights can guide risk mitigation and stakeholder trust strategies to smooth the path for future technology adoption, representing early-stage governance research rather than a deployable commercial asset.
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The study investigates data governance challenges within AI-enabled healthcare systems, focusing on Project Nightingale as a case study to elucidate the complexities of balancing technological advancements with patient privacy and trust. Utilizing a survey methodology, data were collected from 843 healthcare service users employing a structured questionnaire designed to measure perceptions of AI in healthcare, trust in healthcare providers, concerns about data privacy, and the impact of regulatory frameworks on the adoption of AI technologies. The reliability of the survey instrument was confirmed with a Cronbach's Alpha of 0.81, indicating high internal consistency. The multiple regression analysis revealed significant findings: a positive relationship between the awareness of technological projects and trust in healthcare providers, countered by a negative impact of privacy concerns on trust. Additionally, familiarity with and perceived effectiveness of regulatory frameworks were positively correlated with trust in data, while perceptions of regulatory constraints and data governance issues were identified as significant barriers to the effective adoption of AI technologies in healthcare. The study highlights the critical need for enhanced transparency, public awareness, and robust data governance frameworks to navigate the ethical and privacy concerns associated with AI in healthcare. The study recommends adopting flexible, principle-based regulatory approaches and fostering multi-stakeholder collaboration to ensure the ethical deployment of AI technologies that prioritize patient welfare and trust.
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DOI: 10.9734/ajrcos/2024/v17i5441
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