In Tom Dair’s post on Fast Company about the 5 Enablers of Change in healthcare, two are very interestingly about Data:
. Behavior And Health Data
The starting point for behavior change is good information, which means personally-tracked information about things like diet, exercise, and health data collected at home (e.g. weight, blood sugar, blood pressure), as well as a robust record composed of a wide range of information including data from clinical systems (e.g., hospital electronic medical records).
People tend to make good decisions where action and result are closely tied.
There are many solutions for measuring and tracking behavior and personal health information. The celebratedfitbit and DirectLife sensors have software that helps users track things like exercise, sleep patterns, and weight, but don’t really provide a holistic view of data about one’s health. Google Healthcomes at it from the other direction—it’s a pretty robust personal health record, but lacking sensors. (One would have to regularly enter a massive amount of data by hand to get any value out of it.)
While some of us have been thinking and talking about it for years, we’re still in the early days of “the Internet of things.” There are currently only a couple blood pressure cuffs available that you can actually connect to a computer. (Though there is one that can betransfer data to an iPhone, with the promise of another on the way soon.)
2. Assessments From This Data
While there’s strong evidence that just tracking personal data can have a significant impact on health-related behavior, what is done with and in response to the data is critically important. Interfaces into this personal health information must help people understand what diseases and conditions they are at risk for based upon clinical and personally tracked information. And of course, the information must be presented in a way that people understand, quite possibly with health literacy assistance. (We’re not doing well in this department, either).
One of the simplest and most useful implications to be drawn out of all this data is the correlation between behavior and health measurements. Obviously, the relationship between what someone has been eating and their weight gives them good information about whether they should modify their diet. A well-designed system could also tell them how to modify their diet.
Web apps like Daytum and Open.Sen.se allow people to integrate data from a variety of sources to use visualizations and mashup tools to find meaning in the data. While the services are pretty amazing fora quantified-self aficionado, they’re not going to work for someone only willing to spend five minutes a day to stay on top of their health