John Lilly spoke about the progression of quantified self on TechCrunch and described its evolution in four stages:
- Instrumentation – ubiquitous deployment of sensors
- Data collection – aggregating all this data into databases for analysis
- Correlation – performing individual data analysis
- Population Analysis – aggregating individual data analysis for the benefit of a health system (Kaiser), government or medicine more broadly
I believe that quantified medicine is inevitable and essential to reduce healthcare costs and to improve treatment effectiveness. As the quantified self movement matures, we will have to answer questions like:
- Who does the data belong to? To the user? The service/web site collecting the data? To the insurer who pays for the data collection, and who may use it to price a policy? The government? Data privacy ownership questions are springing up all over – Google unifying their cookies, Facebook’s use of social media data, advertising cookie data, and so on.
- How do we secure and anonymize this data? To achieve large scale benefits of population analysis, users need to contribute data in an anonymized way and security will be critical to ensuring sensitive health data isn’t compromised.
- Will doctors evolve into data scientists/statisticians? I dream of visiting my GP for my annual physical where we sit in front of a laptop and run through data. Today, the blood tests are the only interesting insights my GP provides. Tomorrow, it will be correlations between diet and energy, vitamins and performance, travel and happiness. But not many doctors have training to deliver healthcare in this way.
- Will there be a black market for healthcare data, an equivalent of today’s black market for credit card data?