The Influence of Health Data Spaces, Data Repositories, Data Collectives, Data Natives, and Data Commons on Artificial Intelligence for Clinical Decision-Making Among Physicians

Abstract

This study investigated the perspectives of physicians regarding the influence of Health Data Spaces, Data Repositories, Data Collectives, Data Natives, and Data Commons on Clinical Decision Making in Malaysian Healthcare. In particular, this study addressed the mediating effect of artificial intelligence (AI) on the relationship between these health data concepts and Clinical Decision Making. A descriptive, analytical, cross-sectional study was conducted in public and private hospitals in Malaysia. The research population entails physicians with experience handling EMR. The sample included 160 participants. The data were collected using a researcher-made questionnaire and analyzed using the SPSS software using descriptive and Pearson Correlation tests). Health Data Spaces, Data Natives, Data Collectives, and Data Commons showed a significant fair correlation with clinical decision-making, while Data Repositories showed a moderate correlation. Additionally, when AI is introduced as a mediator, the correlation coefficients generally increase, indicating a stronger relationship between the health data variables and clinical decision-making. The study emphasizes the importance of policymakers investing in AI-driven platforms for collaboration between healthcare organizations and technology developers, offering crucial insights to empower healthcare providers in leveraging AI for improved patient care, streamlined processes, and enhanced clinical decision-making. This research holds significance in steering the advancement of health data and AI initiatives focused on enhancing patient care and outcomes. It stands out as one of the limited endeavours that investigate the impacts of novel concepts, such as Health Data Spaces and artificial intelligence, on clinical decision-making within the Malaysian healthcare system.