This project provides an overview of a data model in the scope of sensing data for wearable and mobile devices. It introduces a data model called oHealth-Context which stands for Open Health Context.
oHealth-Context is an initiative proposed as part of the SmartSDk project track HealthCare-App. It aims to benefit a range of individuals and organizations (e.g., patients, researchers, health care system, government, etc.) by reducing the complexity of health data. It consists of a standardized set of schemas and software stack that might facilitate writing applications that can process and create clinical data, regardless of where the data comes from.
The interpretation of clinical data is critical and needs to be associated with proper metadata. For instance, it is imperative for a physician to differentiate between glucose levels among diabetic patients, or to distinguish whether a heart patient is self-reporting his heart rate or whether it is automatically collected during resting or exercise time. In this context, oHealth-Context provides useful data structures to facilitate a meaningful interpretation of the data.
oHealth-Context is built based on an open-source health set of specifications called: Open Mobile Health; which consist of a set of tools and specifications designed to handle medical data.
Projects like the Precision Medicine Initiative and the deployment of private services like Apple’s research-kit & care-kit, Fitbit, Google fit, highlights the relevance of having a standardized and interoperable dataset. Hence, current data schemas are paramount to the semantic interpretation and complexity of health data. In this context, the primary concern of mobile sensing is to concentrate reliable datasets, which requires a shared vocabulary of terms and relationships. Other similar initiatives include DataOne; that provides a set of data-models and specifications in the context of environmental data, OHDSI; which provides a set of data models and set of tools (e.g., API) to handle data based on healthcare.
In this project, we have adopted and extended the standardized mHealth specifications to cover the need of oHealth-Context on FIWARE.
Data schemas specify the format and content of data, such as blood glucose readings, blood pressure, body fat percentage, calories burned, among others; which affects how pre-defined programs might compute data. On the other hand, data usually comes from different devices and platforms, which enrich but difficult a more interesting across-data analysis. Thus, a standard schema serves as a single source of documentation that can be referenced whenever and wherever the data points are used.
The schemas defined in this document are meaningful in distinctions for each clinical measure. For instance, to sense heart rate data, blood pressure, sugar level, so forth, increasing the overall clinical utility of digital health data and improving the ability of developers to build clinically beneficial outcomes quickly. As proof of concept, we have developed a mobile phone application, named MA-Test. Thus, we have defined a reduced and specific set of schemas aimed at measuring the risk of fall for an older adult; as described next.
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