If data are tracked and no one analyzes the information, does it make a sound???
In the last few weeks I have read numerous comments and articles that raise fair questions about the value of self-tracking or the quantified self “movement”. It reminds me a little of the early days of Microsoft and Apple arguing over which was more important, hardware or software. Self-tracking without analysis and learning from the effort is a little like buying an early PC and not buying software to run on it.
The April 1 article by Thomas Goetz in the Atlantic, “The Diabetic’s Paradox”, describes the challenges patient’s with diabetes face self-tracking their disease. He compassionately articulated the difficulties that self-monitoring causes for individuals with diabetes and quotes one study where many “regard self-monitoring as an enemy” and as a result compliance with self-tracking is low. Mr. Goetz then compares diabetes self-tracking with the new wave of self-monitoring being “recommended as a panacea” for other diseases. He asks if there are lessons to be learned from the group that has the longest track record with self-tracking. He closes with three lessons for self-tracking design by saying it should be effortless, consumer oriented, and address the emotional needs of the patient.
I agree with Mr. Goetz’s recommendations and I offer two more. Data collection without integration and analysis is futile. I echo the comments from the Strangely Diabetic Blog where Mr. Strange states, “Data by itself, with no analysis, is totally meaningless.” First, we have to find ways to make it easy for the individual to integrate information from several different sources. Then most importantly, we must give them a tool to aid in their analysis and insights. To date, little has been done to help individuals integrate and gain wisdom from their own EHR, activity, dietary, and medical self-tracked information. We have to give them meaningful and actionable insights for going through the physical and emotional hassle of collecting data.
New data and ways of capturing them are readily available. The challenge is that all of the data are in silos and cannot be easily integrated for analysis. We have to focus our efforts on ways to identify, integrate and mine disparate data sets to help individuals improve their own health. In biology there is a term called “hybrid vigor” or “heterosis” which refers to an offspring’s superior qualities or vigor that result from crossbreeding genetically diverse parents. Could it be that there might be an “Information Hybrid Vigor” or “Information Heterosis” realized by bringing together individual centric medical history, activity, and nutrition data to find successful patterns of behavior? Can we build an interpretative analysis engine that would recommend small behavior changes that would yield leveraged health benefits?
The time is now. We need to give the individual the tools to better understand where they are in their disease and deliver daily insights about small incremental changes that can be made to improve their diabetes outcomes.
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I focus my efforts on ways to identify, integrate and mine disparate data sets to help individuals improve their own health. Today most healthcare analysis is provider, facility, or geography centric and focuses on overall performance or practice/facility/unit variation in care. All are worthy topics, however, few seem to be focused on the individual. Can the same data sets be utilized to build an interpretative analysis engine that would recommend small behavior changes to individuals which would yield leveraged health benefits? Can we mine the currently available healthcare data to help individuals answer, "What are the small changes that I can make in my life that will have the largest impact on my overall health?" …Read More
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