Data scientists in the health sciences work primarily with data generated during experiments, from specialized machines such as next-generation gene sequencing devices or mass spectrometers. But there’s a data type becoming increasingly important to discovery in health care: patient-generated data.
Patient-generated data arises from observations that can be made only by the individual, often during the course of everyday living. Most often these data are self-reported, using a rating scale that solicits the individual’s perceptions, for example, of pain levels or sleep quality. Devices can also be used to acquire patient-generated data, such as a glucometer that captures the concentration of glucose (sugar) in the blood. I consider these “professionally-defined, patient-provided” data.
There’s another type of patient-generated data, one I’ll call “patient-defined, patient-provided.” One example comes from my friend’s mother. She calls one specific data element “wooziness,” and it refers to the feeling she gets when she’s dehydrated. Her thinking slows, and she has a sense of discomfort.
It’s not professionally defined, but “wooziness” provides her with important health information. It heralds the need to act, in her case, to drink water. She can talk about feeling more or less woozy; she knows which days she felt woozy; and she’s beginning to understand what causes it and what mitigates it.
Such patient-generated data do not follow the rules of formal terminologies—these data might not even map to specific anatomical or physiological phenomena—but they tell us something about the person and his/her health experiences.
And that makes patient-generated data powerful.
They give us a window into the person’s everyday health experience. They provide a valuable complement to professionally obtained data, such as clinical observations or biological samples, and they form the basis of assessing patient-reported outcomes, an increasingly accepted indicator of the quality of care.
And since one significant goal of data science is to better characterize the context of health, I believe increasingly incorporating patient-generated data can help us do that.
Like everything in the realm of data science, that will come with its challenges, but for now, if the data you’re working with were contributed by a person, give a silent word of thanks to your invisible partner in discovery. I know I do!