The purpose of this study is to produce new knowledge concerning the perceived user’s value generated using machine learning technologies that activate data network effects factors that create value through various business model themes. The data network effects theory represents a set of factors that increase the user’s perceived value for a platform that uses artificial intelligence capabilities. The study followed an abductive research approach where initially found facts were matched against the data network effects theory to be put in context and understood. The study’s data was gathered through semi-structured interviews with experts who were active within the research area and chosen based on their practical experience and their role in the digitization of the healthcare sector. The results show that three out of six factors were fully realized contributing to value creation while two of the factors showed to be partially realized in order to contribute to value creation and that is justified by the exclusion of users' perspectives in the scope of the research. Lastly, only one factor has limited contribution to the value creation due to the heavy regulations limiting its realization in the health sector. It is concluded that data network effects moderators contributed differently in the activation of various business model themes for value creation in a general manner where further studies should apply the theory in the assessment of one specific AI health offering to take full advantage of the theory potential. The theoretical implications showed that the data network factors may not necessarily be equally activated to contribute to value creation which was not initially highlighted by the theory. Additionally, the practical implications of the study’s results may help managers in their decision-making process on which factors to be activated for which business model theme.