When mHealth apps access more than just patient-entered data in a mobile device, the mix could provide deeper, more powerful clinical data analytics.
Mobile apps such as Daily Carb, Glucose Buddy, SkinKeeper, Pregnancy Tracker and Fitbit have been popular in the consumer market. These apps are making a difference in patient health, empowering the self-tracking of important health-specific, dietary and fitness data.
Healthcare professionals also are seeing the potential that mHealth apps have in helping patients improve outcomes. Currently apps are designed to specifically capture a limited number of data elements. Patients enter the data points manually, or they are captured through a sensory device such as a glucose level reader. But how far can data mining, social media and patient engagement push the clinical relationship?
An emerging generation of mHealth apps is using more than just patient-entered data to monitor health. For example, Ginger.io collects and analyzes hidden data such as messaging logs onboard a mobile device to help better understand patients. The company’s website explains that the concept is to mine iOS or Android data to monitor user behavior and identify changes to the user’s health — information that can be pushed to a healthcare provider who might need to step in.
There is clearly a significant amount of data analysis and calculation happening in the background of such an app. Enabling these processes requires review of the data to identify any discrepancies. In conjunction with that, the app prompts users with a survey to capture specific data points.
Having sensory data and other insights — such as mood and mental state — can provide a wealth of information for data scientists.
This approach may prove to be a much more comprehensive one.
By monitoring active and passive patient data on a daily basis, both patients and providers can discover significant changes in behaviors and create much closer relationships. Furthermore, it’s likely that social media sites could end up becoming a valid data source for monitoring an individual’s activities and moods.