I was reading an article about the controversial Dr. Oz this morning when a quote from a doctor struck a nerve. In reaction to Dr Oz’s embrace of alternative medicine, he stated: “I’m guided by the evidence.” That’s a wonderful and comforting sentiment to any logical person. We have a methodology called science which helps us move towards the truth through a repeated, disciplined process of experimentation. This process allows us to build confidence in our opinions and actions when we have accumulated sufficient evidence or can appeal to previous authority. The problem is that evidence in medicine is rarely imbued with absolute authority, yet the dogma of medicine is that peer-reviewed journal results are the primary guide to treatment. Clinical trials should be viewed as the starting point in the practice of medicine, not the destination.
The family doctor or nurse’s job is an impossible one. Given our imperfect description of symptoms, and possibly a blood test, they have to figure out what intervention will help us return to a stable, healthy life. The problem is that human beings are probably the most complicated system that we can imagine to try to regulate. Our environment, psychology, and life habits interact deeply with dozens of major internal organs and body systems which manifest tens of thousands of possible known problems. We know a great deal about the components of our body, but what we do know is dwarfed by what we don’t know about how these components interact with one another. After a brief digression into what makes medicine a hard problem, I’ll introduce some ideas for what we, as patient participants, can do to improve the problem solving process, our own care, and healthcare at large.
A recent TEDMED talk by Albert-László Barabási highlights the rich system of interconnected networks the exist both within and outside our body; networks are a way of viewing the world that we are only beginning to understand. The functioning of our body is not independent from our mind and environment. The bugs in the soil communicate with the symbiotic bacteria that enable us to digest food, the micro-RNA of the plants we eat may directly change our gene expression, the inactive ingredients in breast milk line a baby’s intestines and protect it from external pathogens until the immune system has matured, and our expectation of a treatment’s effect can physically transform how our bodies function (the so-called placebo effect).
Recently I’ve been observing via RescueTime that I spend 3 hours or more hours in my e-mail application most days. However, I don’t have a good breakdown of how much of this is scheduling, looking up information, commenting on something substantive or social discourse. There is a tremendous amount of information locked up in the time-series of e-mail’s sent and received that can provide insight into aspects of my behavior such as focus of attention (time of day e-mail is sent), social relationships (what organizations I interact with in a given week), the density of idea generation, etc. E-mail logs contains a wealth of raw data that can be instrumented to uncover important information about our life.
Our E-mail logs are also rich archive of useful information such as phone numbers, addresses, what we said to someone, when we said something to someone, edits to papers, attachments, etc. With a proper set of tools, many of which have been built for analyzing social media, we can turn this archive into a database of useful information that can significantly enhance e-mail-based instrumentation.
A discussion I had earlier today reminded me of an argument I’ve had with friends in the scientific community on multiple occasions. The argument revolves around the belief that conclusions of science, such as the effect of cholesterol on heart disease, suggests specific interventions, such as reducing the dietary fat that we believe causes high cholesterol. In essence, we debate the means by which new scientific evidence should be used to influence public policy and private behavior. Taking strong evidence of a specific causal link between a cause and an undesireable outcome as prescription for a population intervention to remove the causative factor is fraught with danger. There are many reasons for this, but the two most salient are confounding and the law of intendended consequences.
After a summer travel hiatus to work on research projects, I’m back on the road again this week.
Today, I’m chairing a break-out panel at Mayo Transform 2011 to talk about the power and opportunities in sharing health data across organizational and discipline boundaries. Tomorrow I’m participating in a break-out discussion featuring Hugh Dubberly on self-tracking and visualization, also at Transform. Check out the alternative announcmeent on the fantastic new Lybba.org website and blog.
On Thursday I’m participating on a panel about Innovation Scaning sponsored by the Office of the National Coordinator for Health Information Technology and the Health 2.0 team in DC.
It feels great to exchange ideas in real-time with my collaborators the larger health innovation community again. As always, Transform is off to a great start!
Over the past year, I have had the pleasure of advising the non-profit organization Lybba. Lybba’s vision is closely aligned with the work we’ve been doing at New Media Medicine. This month I accepted a one year Research Fellowship with Lybba; the objective is to complete and apply my PhD research in the context of Lybba’s ongoing projects. Chief among these projects is the Collaborative Clinical Care Network (C3N) which I wrote about a few weeks ago.
I’ve been extremely impressed with the scope of their ambition, the quality and breadth of their team and partners, and the concrete projects they’ve chosen to invest time in. Continue reading
Nearly one quarter of US adults read patient-generated health information found on blogs, forums and social media; many say they use this information to influence everyday health decisions. Topics of discussion in online forums are often poorly-addressed by existing, high-quality clinical research, so patient’s anecdotal experiences provide the only evidence. No method exists to help patients use this evidence to make decisions about their own care. My research aims to bridge the massive gap between clinical research and anecdotal evidence by putting the tools of science into the hands of patients.
Specifically, I am enabling patient communities to convert anecdotes into structured self-experiments that apply to their daily lives. The self-experiment, a type of single-subject (N-of-1) trial, can quantify the effectiveness of a lifestyle intervention for one patient. The patient’s challenge is deciding which of many possible experiments to try given the information available. A recommender system will aggregate experimental outcomes and background information from many patients to recommend experiments for each individual. Unusual interventions that succeed over many trials become evidence to motivate future clinical research.
I’m sharing the current status of my proposal to invite feedback and discussion.
I just returned from spending a day with the team working on the Collaborative Chronic Care Network (C3N) who are part of the amazing ImproveCareNow (ICN) network of clinics as well as some very creative visionaries building the Anderson Center of the Cincinnati Children’s hospital.
ICN/C3N is focused on helping the families of children with Crohn’s disease or other IBS/IBD diseases like Ulcerative Colitis. In recent years the team has focused on improving care delivery by showing how a network of centers can systematically improve care delivery by being disciplined in measuring and sharing outcome data. They actively seek to translate learnings from over and under performing centers or sub-populations to change care delivery across the network and effect a shift in the mean outcome curve for chronic disease.
More to the point, they are actually implementing the data collection, cross-institutional transparency and systems processes we all talk about. Continue reading
What is Self Tracking??????????
Self-tracking is a process through which we attempt to uncover patterns in our daily lives or environment. Tracking can be used for a variety of purposes, including exploratory (what correlations do I see), explanatory (why does this happen) or experimental (If I change X, Y should happen). Regardless of the specific purpose, our ultimate goal is almost always to develop some model of cause and effect that we can use to inform our future decisions. The discovery of cause-effect relationships and the consequences of interventions is the essential aim of the scientific method. It takes years of education and practical training to understand how to apply methodology to gain valid insights into fundamental questions about cause and effect in some natural or artificial system. Methodology is crucial to avoid developing incorrect conclusions.
However, we must also acknowledge that tracking, modeling and intervening in system is a fundamental human exercise. Continue reading