Patient Generated Health Data: Framework for Decision Making

Patient information is a major part of healthcare decision making. Although currently scattered due to multiple sources and diverse formats, decision making can be improved if the patient information is readily available in a unified manner. Mobile technologies can improve decision making by integrating patient information from multiple sources. This study explores how patient generated health data (PGHD) from multiple sources can lead to improved healthcare decision making. A semi-systematic review is conducted to analyze research articles for transparency, clarity, and complete reporting. We conceptualize the data generated by healthcare professional as primarily from EHR/EMR and the data generated by patient as primarily from mobile apps and wearables. Eight themes led to the development of Convergence Model for Patient Data (CMPD). A framework was developed to illustrate several scenarios, to identify quality and timeliness requirements in mobile healthcare environment, and to provide necessary decision support

Approaches to mobile health evaluation: a comparative study

A major challenge faced by mobile health (mHealth) is identifying an evaluation technique which provides a rigorous evaluation while capturing the unique characteristics of the intervention. This study investigates traditional and emerging methods of mHealth evaluation, identifying existing gaps. This research is a useful first step toward developing an evaluation technique which will facilitate implementation and enable mHealth to reach its potential in accelerating socio-economic development, particularly in Low and Middle Income countries (LMICs)