Two major scientific advances are moving along in parallel, but will both benefit from intertwining. I’m talking about genomics/genetics/proteomics on the one hand and connected health on the other. While the genetics people have popularized the term ‘personalized medicine’ for their end vision, one might argue that connected health is just as powerful for personalizing medicine and has the added value of allowing us to do population-level tracking that we could only dream of in the past.
Lets unpack all of this.
When we are able to study all the genes of a cell or tissue at the DNA or protein level, that’s genomics. Together with the large-scale study of the structures and functions of proteins, known as proteomics, and genetics or the science of heredity, clinicians are able to map out such specifics as a person’s predisposition for certain diseases. Highly individualized information that could change how we treat patients.
The genetic/genomic revolution has been maturing for years now. At the start of my career, I had a brief stint (~ 5 years) as a laboratory investigator. Sequencing just a few hundred base pairs of DNA would take overnight. These days, with high throughput technology, an entire genome can be completed in a few weeks. Cost is dropping too. Companies like Illumina and 23andme offer genome sequencing at around $10,000. Several pundits estimate that by 2015 you will be able to get your whole genome sequenced for about $1,000.
So what, you ask? Imagine a world where a provider can tell you with near certainty how you will respond to a certain medication (typically we hear things like ‘you have a 60% chance of X outcome’) or with great certainty what your risk of contracting a certain condition is. We are so used to hearing our health talked about in percentages that we don’t stop to think that for us its either 100% or nothing. For so much of our health forecasting, knowing our specific genetic make up will change that.
Now consider the implications of connected health. By collecting objective measurable information about you and sharing it with you to inform your self-knowledge and improve your health behaviors, we are improving patient adherence, better engaging patients in their care, and helping clinicians improve clinical outcomes. Here again, highly individualized information, in this instance, changing how we deliver care.
The implications of this concept were discussed in thoughtful detail in a piece in the New York Times Magazine on May 2, 2010, by Gary Wolf, called “The Data-Driven Life”. He points out how a connected health-type approach can allow an individual to do a ‘clinical trial of one’, i.e., to develop an hypothesis about their health, test it and objectively measure the results.
This last weekend, on Saturday, I cycled for one hour and on Sunday I did yard work for about 2 hours. My tendency had been to assume that cycling was a more vigorous activity than picking up pine cones and running the edger/trimmer after my daughter finished cutting the lawn. However, a check on my Bodymedia armband website educated me otherwise. The lawn work burned almost twice as many calories.
Really, another way to think about it is while genomics will very soon be giving us our own unique genotype, or a blueprint of our genetic makeup, connected health will be able to give us our own phenotypic map, characteristics and influences that will affect our health behavior. They are complimentary and, together, could be very powerful tools in managing illness.
Now, what about the population health part? We’ve just started to play around with this at our Center. We were inspired by some of the graphic represenations that the folks at RxVitality are showing regarding their Glowcaps device. So we started to look at our own data at the population level.
Population-level connected health data does two fascinating things. One is that it allows a provider to intervene ‘in the moment’ to address a health issue that may need timely intervention. Exceptions to a given set of health parameters can be flagged for rapid intervention. We’ve seen this value in our own programs in both heart failure care and diabetes. The second is that one can look at various populations of patients and cull out aggregate effects as well. For instance, in one practice, we noticed that their diabetics were doing well as a group when we looked at their morning glucose levels, but not well at all as a group when we examined the afternoon levels. Is that a teaching effect? A prescriber’s habit?
These population views tell us more about how we’re doing as providers while the individual data help the motivated individual improve their own health. Both of these phenomena are part of the solution to the healthcare problems facing us today.