Patient-generated health data analytics on ambulatory behaviours of people living with T2D: A Scoping Review (Preprint)

BACKGROUND Complications due to Type 2 Diabetes (T2D) can be mitigated through proper self-management which can positively change health behaviours. Technological tools are available to help people living with T2D manage their condition and such tools provide a large repository for patient-generated health data (PGHD). Analytics can provide insights about the ambulatory behaviours of people living with T2D. OBJECTIVE The objective of this review was to investigate analytical insights can be derived through PGHD with respect to ambulatory behaviours of people living with T2D. METHODS A scoping review using the Arksey & O’Malley framework was conducted in which a comprehensive search of the literature was conducted by two reviewers. Three electronic databases (PubMed, IEEE, ACM) were searched using keywords associated with diabetes, behaviours, and analytics. Several rounds of screening using predetermined inclusion and exclusion criteria were conducted and studies were selected. Critical examination took place through a descriptive-analytical narrative method and data extracted from the studies was classified into thematic categories. These categories reflect the findings of this study as per our objective. RESULTS We identified 43 studies that met the inclusion criteria for this review. While 70% of the studies examined PGHD independently, 30% of the studies combined PGHD with other data sources. The majority of these studies used machine learning algorithms to perform their analysis. Themes identified through this review include 1) predicting diabetes / obesity, 2) factors that contribute to diabetes / obesity, 3) insights from social media & online forums, 4) predicting glycemia, 5) improved adherence / outcomes, 6) analysis of sedentary behaviours, 7) deriving behavioural patterns, 8) discovering clinical findings, and 9) developing design principles. CONCLUSIONS The increased volume and availability of PGHD has the potential to derive analytical insights regarding the ambulatory behaviours of people living with T2D. From the literature, we determined that analytics can predict outcomes and identify granular behavioural patterns from PGHD. This review determined the broad range of insights that can be examined through PGHD, that would not be available through other data sources.

Digital Health Innovation Enhancing Patient Experience in Medical Travel

Digital health technological innovations are disrupting every sector of the economy, including medical travel/tourism. Global patients as medical tourists are using patient-centric digital health technologies, enhancing patient/medical tourists experience and making it more transparent and engaging with healthcare providers and medical tourists. Digital communication tools such as e-mail, online appointments, smartphones, instant messaging applications, social media tools, user-generated content by online patient communities, tele-medicine, tele-radiology, my-Health records, Skype consultation, WhatsApp, health video, electronic health records, health data analytics tools, and artificial intelligence-enabled health technologies enhance the medical travel decision-making process, reduce cost, improve patient care and transparency of communication, and engage the relationship between the patient and the healthcare provider with positive outcomes, medical tourist experience, and empowerment.