Toward a more sustainable cost of healthcare one approach has been a focus on preventive healthcare. My read in this month’s TIME magazine showcased the implementation of what I’d call “preventive dropout” – a non-profit organization, City Year, having voluntary 17-24 year-old successful individuals assist in practicing pro-education anti-dropout operations in schools grades 3-9 to kids that are identified as likely to dropout. Based on what they call the ABC’s – poor Attendance, disruptive Behavior, and Course failure in Math and Science, City Year uses these indicators to pick out future dropouts and catch them early – an opportunity to instill experiences in the classroom early, and thus effective, toward prevention of future dropouts.
The average wait time to get a doctor’s appointment is 3 weeks – an inaccessibility to healthcare that drives people to turn to the internet for information about their experienced symptoms. At the realm of this, WebMD has met with success but I’ve always had a problem with WebMD’s “textbook” listings of possible diagnosis for the symptoms you experience. It still leaves you to determine how the collection of your symptoms decipher the most probable diagnosis, that is if you are willing to read through several articles related to your symptoms. A major resolution of this issue is the Baltimore-based Sympcat.com that has used data from the Centers for Disease Control to generate an algorithm that will curve your step by step symptomatic input toward a listing of diagnoses – with impressive probability numbers as well.
I am often asked about why knowing the complete human genome isn’t enough to treat mankind’s diseases. Knowing what gene is missing and replacing that gene through gene therapy seems a logical approach to curing diseases, but biology may not respond to logical approaches; One reason may be due to the “contextual-reprogramming” of the mammalian genome. The human genome has close to 20,000 genes. To advance therapeutics the field has sought to recognize what many of these genes do. Through reverse genetics, companies and academic labs will delete specific genes, generate animal models with the deleted genes, and recognize the behavioral outcomes of those deletions; knowledge of which can spur potential genetic or protein targets to treat human disorders. While this has proven success in a few cases, therapies that replace the disease genes often prove to not cure the diseases. The major complication with such a logical approach is the immense reprogramming of some of the 19,999 genes when in the context of the deleted gene of interest (context genes). Consider a funnel that you fill with 15 rocks (genes): each one has its specific position and function that is specific to that position. If you were to remove one rock positioned somewhere in the middle of that funnel you’ve done more than just remove one rock and its function; you’ve now altered the position of a few of the remaining 14 rocks in the funnel (and yes gravity is necessary for this analogy). This “displacement” response in genes makes therapeutic approaches challenging as alterations in many of the 19,999 genes may be the causes of the disease, and their altered functions irreversible when the target gene is therapeutically replaced. The challenge in the field is how to de-displace these displaced genes or their protein products so that therapies more directly treat the diseases.
Over the recent years there seems to have been a great push for better “insight” into the psyche of the consumer. This of course is by manufacturers and marketing firms desperate to shelve the archaic survey-and-questionnaire-based data the field has been using for decades. Take for example a scenerio where a company is interested in learning whether customers would prefer a yellow-colored toothpaste or a white-colored one. While the survey-and-questionnaire method may proove to suggest some clear preference, manufacturers and marketing firms are often handed results that seperates the preferences by a mere 5%. In addition to this across-consumer variability, consider the within-consumer variability – you may prefer the white toothpaste today but the yellow one tomorrow, next week, or even a year later. Why is human choice so variable and volatile? One explanation may be the incredible adaptability of your brain – an evolutionary capacity to not just remain stagnate but to calculate your experiences over and over again – re-analyze them, re-define them. Consider a painful experience you’ve had. Would you rather have the brain capacity to maintain the hightened pain associated with that experience or would you rather have the brain capacity to attenuate that pain so that you carry on with the herd? The brain is more of the latter and it is precisely this malleability that makes it remarkable and inaccessible through conventional survey-and-questionnaire methods.