Malicious Learning has been introduced to encompass erroneous learning that occurs in Machine Learning when erroneous data enters the training sets deliberately or through noise or miscoding. In the social domain, the term counter-knowledge has been used to refer to misinformation, gossip, rumours, and conspiracy theories that masquerade as knowledge. Counter-knowledge leads to inappropriate individual behaviours and organisational decision-making. Therefore, an individual’s assimilation of such counter-knowledge can have various harmful consequences for his organisation. This study proposes a framework for investigating the relationship between counter-knowledge and learning myopia at the individual level in the healthcare domain, focusing on Multiple Sclerosis (MS). Given that those suffering from MS can experience symptoms leading to both a slowing down of information processing and a limited capacity. It is argued that these symptoms are likely to lead to exacerbating other symptoms, such as anxiety and depression. In addition to investigating how social counter-knowledge results in individual counter-knowledge, a set of strategies to disrupt this linkage is proposed.