## 1 Introduction  
Information technologies are reshaping the competition in enterprises and are leading to a global knowledge society [1]. In order to achieve a competitive advantage, organizations need to be prepared for and regularly updated on future trends and applications with the help of business intelligence (BI) tools [2]. In this vein, the use of machine-learning techniques is becoming irreplaceable in knowledge management [3, 4] from Industry 4.0 in general [5] to healthcare [6], spanning a wide range from predicting diseases [7, 8, 9] to high-level knowledge extraction [10, 11]. Enterprises can be considered as capital-intensive enterprises or knowledge-intensive enterprises [12, 13], and knowledge-intensive enterprises attract intense attention in knowledge-management research [14]. Studying the dimensions of knowledge management orientation and analyzing the survey data from managers of large firms, [PERSON] showed that the major factor of internal process performance is knowledge sharing, and that financial performance is affected by non-financial performance measures such as learning and growth [15]. Through a comprehensive literature analysis bridging the professional complex systems and knowledge network structure, [PERSON] found that, contrary to complex systems where the effective knowledge-sharing networks may be richer in density, it may be richer in brokerage and hierarchy in the professional complex networks [16]. Based on empirical data, [PERSON] and [PERSON] studied knowledge management in academia--industry interactions and found negative effects of collaborations with industry on knowledge sharing of researchers [17]. Via integration of the open innovation and knowledge-transfer literature, [PERSON] et al. developed a framework for effective knowledge translation mechanisms, in particular for the R&D projects [18]. Due to the dynamic and complex nature of knowledge systems, managing knowledge which might not be predictable or controllable requires an intense effort [19; 20], advanced theories and framework development [21]. Leadership in knowledge-intensive firms is also a center of attraction in knowledge-management research. Very recently, focusing on the thought leadership in knowledge-based organizations, [PERSON] et al. addressed the problem of providing a comprehensive definition of the thought leadership, found specific tensions as a consequence of creating thought leadership and developed a framework for the engagement of thought leadership at various levels [22]. Extracting knowledge from medical or clinical documents through developing frameworks based on NLP has been a key direction in the field, and transformer models are attracting increased attention. For example, the Bidirectional Encoder Representations from Transformers (BERT) [23] model was used by [PERSON] et al. to learn contextual embeddings from free-text triage notes of a French hospital's emergency department [10]. BERT models were also used by [PERSON] et al. to extract knowledge from biomedical clinical notes [11]. While extracting and managing knowledge in the healthcare industry has already been taking advantage of developing frameworks based on various natural language processing (NLP) techniques [24], there is still a great deal of room for research and development. Based on a two-decade experience of NLP practices at the Mayo Clinic, [PERSON] et al. recently pointed out room for improvement by developing a framework for stressing the potentials of NLP in healthcare [25]. As social media platforms have already become a major information source, NLP techniques are recently being employed to extract knowledge from the communications among patients and healthcare professionals on social healthcare networks [26]. Aiming to contribute to knowledge management in the healthcare industry, the present work is based on the following steps. First, in order to understand the need for BI systems and the capabilities of business intelligence software, we interviewed 32 experts.