Open mHealth and the Problem of Data Interoperability

Abstract

Abstract The words we use to describe data, both popularly and in the social sciences, often imply that data remains comprehensible as it moves around: “data flows,” “data exhaust,” “seamless,” and the like. However, there are more steps than we ordinarily assume between “raw” sensor data and data that is helpful for making judgments about a medical condition. Making data legible as it moves from one system to the next is a nontrivial technical challenge, even before we get to the social challenges. In this chapter, Dawn Nafus interviews Deborah Estrin about the realities of building systems for supporting meaning-making across technical systems. Estrin is a computer scientist at Cornell University and cofounder of Open mHealth, a nonprofit startup that aims to bring clinical meaning to digital health data through an open platform designed for improved interoperability among disparate, heterogeneous data sources.