Using of Patient-Generated mHealth Data for Patient Care: a Comparison of Four Models

Abstract

The rapid adoption of mobile applications for wellness and health tracking has resulted in vast amounts of patient-generated data. However, these data are often underutilized in the traditional patient care. In this paper, we explore how to use these patient-generated data to improve patient care. Based on a review of healthcare model and recommendations, we propose and compare four models with increasing integration with electronic health records. We also compared the freedom of choice of apps, as well as content validity and expected effectiveness. In the first model, patients have the full range of app choice, and full control over their data, in particular for sharing with healthcare providers. In the second model, patients use a selection of apps to export their data to a repository, which can be accessed by their providers (without integration into the EHR). In the third model, interoperability between the apps and the EHR allows full integration, but restricts app choice. Finally, the last model adds the notion of cost-effectiveness to the previous model. Although the EHR-integrated models limit app choice for patients, the app content is medically validated and patient-generated data is more easily accessed to improve patient care. However, these integrated models require decision support algorithms to avoid overwhelming the healthcare providers with data, and may not necessarily imply better quality patient care.