Digital health: Patient-generated health data and use of digital technology

Abstract

The digitization and creation of new technologies have significantly increased in healthcare. Digital technologies have become important tools in the relationship between patients, doctors, and the healthcare system. These technologies allow patients to use digital devices to monitor their health, share this data with their healthcare teams, and facilitate decision-making about treatments. Patient-Generated Health Data (PGHD) refers to health-related information produced, documented, or collected by patients, family members, or other informal carers to address health issues. PGHD encompasses various aspects, including but not limited to, health history, treatment history, symptoms, biometric data, and patient-reported outcome measures. PGHD is playing a crucial role in improving the operational efficiency of healthcare systems. By empowering patients with rare diseases or their informal carers to collect and monitor health-related data, they can act as advocates and researchers for their condition. Due to the specificity and complexity of these diseases, in a field of knowledge that is constantly evolving, sharing self-generated health data with providers or other stakeholders may help enhance scientific research and improve clinical benefits. This thesis seeks to explore the willingness and motivations of patients with rare diseases and their informal carers to actively collect their health data and identify their preferred technologies to self-generate data for this task. In addition, it also aims to understand whether rare disease patients and informal carers are willing to share this information and for what purposes. To achieve these goals, a qualitative study was undertaken, and semi-structured interviews were conducted, both by phone and in person, with a sample of 24 patients and 16 informal carers. Preliminary data analysis of interview transcripts was conducted concurrently with data collection to promptly identify redundant information and subsequently stop sampling as necessary using the NVivo software V12. Subsequently, an in-depth analysis focused on the key topics of the thesis was performed, utilizing both deductive and inductive approaches. The study revealed that most rare disease patients and their caregivers are willing to collect health data, with differing motivations between the two groups. For patients, the main incentive was the potential to use the data to improve treatment and disease management, while informal caregivers were more motivated by the opportunity to contribute to scientific research. Most participants preferred using mobile devices for data collection, although some opted for analog methods. Regarding data sharing, the majority agreed to provide self-generated information for biomedical research and personalized medicine but raised concerns about privacy and security. Additionally, there was reluctance to share data for other studies, depending on the research purpose. The thesis highlights the importance of understanding the different motivations and preferences, as well as the concerns, regarding the self-generation and sharing of health data and concludes that by creating solutions to address these identified barriers, healthcare systems, patient treatment, and scientific studies on these diseases can benefit from the volume of this data.