What are common data schemas?
Data schemas specify the format and content of data, such as blood glucose readings, which affects how software programs process that data. Systems often must handle data coming from many different devices or platforms, with each source describing the data differently. It is much easier to process and make sense of data if all data points for a specific measure (e.g. blood glucose) are expressed in a shared, common schema, regardless of where the data came from. A common schema serves as a single source of documentation that can be referenced whenever and wherever the data points are used.
Why are they important?
In healthcare, common data schemas are particularly important because of the semantic importance and complexity of health data. For example, the distinction between fasting and non-fasting blood glucose is critical to its clinical meaning. Similarly, whether a heart rate is at rest or during exercise, or whether a weight is self-reported or automatically measured, are critical distinctions when making sense of data and taking clinical action using that data. Our common schemas define the meaningful distinctions for each clinical measure, increasing the overall clinical utility of digital health data and improving the ability of developers to quickly build clinically usable products.