Computationally-guided Qualitative Analysis of User-Generated Data for Different Models of Mobile-Personal Health Records Apps

Presentation Time: 09:15 AM - 09:30 AM

Abstract Keywords: Personal Health Informatics, Human-computer Interaction, Natural Language Processing, Patient Engagement and Preferences, Qualitative Methods
Primary Track: Foundations
Programmatic Theme: Consumer Health Informatics

Mobile Personal Health Records (mPHR) are smartphone apps granting patients portable and continuous access to their medical records on the go, thereby increasing their potential to play an active role in managing their healthcare. An extensive body of literature has focused on understanding user(s) experiences with web-based tethered PHRs (i.e., Patient Portals) offered by healthcare organizations. However, patients' opinions of smartphone-based PHRs have received less attention. Our study aims to understand this gap. We used a computationally-guided qualitative analysis approach to identify latent topics indicating dimensions of user experiences present in app reviews left on popular m-PHR apps available on Google Play and Apple app stores. After following a detailed app selection process, 10 m-PHR, including tethered (n=6) and interconnected (n=4) apps, were selected for analysis. Our findings show similarities in user experiences for HCO-tethered PHRs and HCO-independent interconnected PHRs, and we discuss the design implications concerning the differences.

Speaker(s):
Zainab Balogun, None
University of Maryland Baltimore County