Guest Post: A Few Thoughts About Patient Health Data
by Emil Chiauzzi, HDE Steering Committee Member and PatientsLikeMe Research Director
With the launch of the Health Data Exploration (HDE) project, we have an important opportunity ahead to understand the role and impact of health technology. We have much to learn from health innovators, and I began thinking about the role of the patient as an active participant in producing “digital footprints.” There are several ways we can promote a “patients first” mentality when considering research on patient-generated data.
First, the opportunities afforded by the collection of passive data are vast because we can infer patient behavior without interfering with the normal flow of daily activity. But passive data collection should not mean that the patient is an inactive participant in the process. Patients need to understand and value the data being collected, and then attribute meaning to those data in order to translate this meaning into an appropriate course of action. We need to know more about whether data collection itself has reactive effects, as has been long observed in the self-measurement of other health behaviors. We also need to know more about the triggering effect of data – how much fluctuation patients need to see before they take action. Are there ways that passive data collection may reduce “behavioral friction” and only surface actionable data when needed?
This leads to a second issue. Wearable devices help the process of quantifying behavior, but the act of quantification may be insufficient to direct the actions that the individual must take. Many wearable device makers integrate behavioral principles into the user experience, e.g., rewards, feedback, encouragement, social, etc. But this is often applied in a generic, rather than individually tailored, manner. It seems that the inclusion of self-defined behavior change programs would be an important next step in the quantified self movement. This would allow researchers to identify the “hacks” and lifestyle adjustments that users develop as a means of maximizing their tracking experience, and enable others to learn these methods and apply them for their individual benefit.
Third, it will be important for our research design to consider “continuous patient engagement” (Mullins et al., 2012) and the inclusion of patients in research, from concept elicitation through implementation and dissemination. This should be a natural outgrowth of the quantified self movement, which places the individual at the center of the measurement process. Although difficult to achieve from beginning to end, we should always strive to systematically capture the patient voice at different stages of research. As self-experimentation increases, having the research processes in place can fine tune these experiments for a more communal benefit.
Finally, large segments of the population are not interested in conscious efforts to collect, monitor, or apply personal health data. As a result, it is important to identify subgroups of potential health data users. How can we better determine who is most likely going to benefit from the rapid expansion of technologies geared toward personal health data collection? Are there ways that quantification can move directly into the self-management of chronic disease and be used by underserved populations? Addressing these questions may point to adaptations necessary to broadly generate and apply personal health data at the individual and systems level.
These are just a few of the many questions that will arise during this project. I look forward to engaging with my HDE colleagues to explore these and many other questions.
