JMIR Publications

ABSTRACT

Online healthcare content has emerged as a primary source for patients to access health information without direct guidance from healthcare providers. This is dependent on patients being able to access engaging high-quality information, but significant variability in the quality of online information often forces patients to navigate large quantities of inaccurate, incomplete, irrelevant or inaccessible content. Personalisation positions the patient at the centre of healthcare models by considering their needs, preferences, goals and values. However, traditional methods used thus far in healthcare to determine factors of high-quality content for a particular user are insufficient. Machine learning (ML) uses algorithms to process and uncover patterns within large volumes of data to develop predictive models that automatically improve over time. The healthcare sector has lagged behind other industries in implementing ML to analyse user and content features, which can automate personalised content recommendation on a mass scale. With the advent of ‘big data’ in healthcare, which builds comprehensive patient profiles drawn from several disparate sources, ML can be used to integrate structured and unstructured data from users and content in order to deliver content that is predicted to be effective and engaging for patients. This enables patients to engage in their health, supporting education, self-management, positive behaviour change and enhancing clinical outcomes.