Article
Examining the Influence of Trust in AI on Patient Satisfaction: Insights from AI-Driven Healthcare
Artificial Intelligence (AI) is transforming the healthcare sector by improving treatment planning, diagnostics, and patient care. As AI become more widely used in healthcare, building and maintaining patient trust in these technologies has never been more important. Patient trust is increasingly recognised as a foundational element that determines whether AI tools are embraced or rejected in clinical environments. This study examines AI’s role by evaluating AI Diagnosis Confidence (ADC) scores, quantifying patient trust in AI, along with biomedical parameters such as heart rate, blood pressure, and recovery time. Built on the Technology Acceptance Model (TAM) and the Health Belief Model (HBM), this study examines how AI affects patient satisfaction and health outcomes, with a specific focus on the role of pharmaceutical companies in advancing AI-driven healthcare solutions. This study uses a mixed-method approach to provide a comprehensive understanding of AI adoption in healthcare. A dataset of 5,000 patient records was collected and analyzed from Kaggle using machine learning techniques such as Gradient Boosting and Random Forests. Additionally, the K-Means clustering algorithm was adopted to group patients based on ADC scores and biomedical data. A thematic analysis of interviews with doctors, AI experts and pharmaceutical professionals was also conducted to identify qualitative insights. Findings of the study reveal that in quantitative analysis, the Gradient Boosting algorithm achieved the highest accuracy of 0.6369, indicating that trust in AI and optimal biomedical parameters have a significant impact on patient satisfaction. Cluster analysis identified three different patient satisfaction groups, showcasing how different patients respond to AI-assisted care. Through qualitative thematic analysis, four important themes were discovered: (1) Transparency - Patients and physicians emphasised the importance of understanding AI's decision-making process to build trust. (2) Human-AI Cooperation - AI should complement human care, not replace. (3) Cultural Acceptance - Institutional validation boosts confidence in AI, while regional variations affect trust. (4) Pharmaceutical Role - To boost patient confidence in AI systems, clinical validation must be conducted by pharmaceutical companies. This study provides valuable insights into building trust in AI by integrating both qualitative and quantitative findings. It highlights the importance of collaboration across technology, healthcare, and pharmaceutical sectors in improving patient outcomes. This research offers a practical framework for healthcare organisations, contributing to the development of more patient-centred and trustworthy healthcare systems.