I am not a 50 year old white male. I am a 50 year old black male. I do not have high blood pressure or diabetes. I am not pre diabetic. I am not obese. My body fat range between 12 to 15 percent depending on the season. My resting blood pressure is 115ish over 60ish with a resting heart rate between 55 to 60 bpm. I do not fit the stereotypical medical norms. Nor do I fit into the insurance actuarial tables.
If you’re asking why is this important? It’s important because my skin color doesn’t match my health statistics. When I see a physician for the first time, they assume I fit into the stereotypical norm of medical issues related to being black and over 50. I’m charged more for medical and life insurance because of my age and skin color. This happens in an antiquated system that relies too heavily on old standards of information.
We’ve heard about using Big Data for better customer diagnosis and care decisions. Unfortunately, most data is used to market goods and services directly to a targeted audience rather than to produce meaningful health outcomes.
Here is a better use of data, which would improve health outcomes and save customers money. George Wallace (the Gov. of Alabama) was on to something when he said “segregation now, segregation tomorrow, segregation forever”. Big Data can segregate like populations examining their health statistics compiled over time to provide accurate assessments and proper diagnosis. This same information could be used by insurance companies to provide better products and pricing.
As a 50 year old black male (50YOBM) who’s consistently physically active over the past three and half decades, I would be segregated into a grouping of similar black males. This segregated group could be comprise of a local, regional, national and even international population. The group could be further segregated by occupation, education, marital status and sexual orientation. There are a number of combinations this grouping could be examined to pull out information specific to the group’s demographics, in an effort to fine tune diagnosis and treatment.
The system is contaminated because the mongrel population has run amuck. Females compared to males, blacks to whites, young to old and any other combination one can think. The only true way to examine a population is by segregation. Fortunately, most studies begun separating women from men a decade ago. These well thought out studies also segregate by age and geography. This is a great place to start, but much more can be examined using Big Data.
I think socioeconomic status may be over emphasized when segregating a group, money increases access to care and ultimately to treatment. Money doesn’t improve wellbeing or predispose a person to being healthy. I think money prejudices research much like a placebo. Big Data removes the prejudices and get to what’s really going on, the unspoken stories.
Most of the data that is being collected is wasted. Information is housed inside a storage unit waiting for someone to access it for its value. There is value in neighborhood data not everyone living on your block behaves like you. Neighborhoods are not homogenous populations. There are different education levels, professions, ethnicities, gender identities and much more. Data researchers can tease out the likes, dislikes and similarities and create usable information for practitioners. What good is having access to Big Data if it’s only going to be used to determine drug interactions?
Treatment Teams and Treatment Groups
Segregation of data would determine best treatment requirements for the 50YOBM group. The treatment team for this specific treatment group might include the following practitioners; a primary physician, orthopedic, a sport medicine doctor, an endocrinologist, a physical therapist, a mental health worker, an exercise practitioner, a dietician and a message therapist. The treatment group might include black physically active males between the ages of 45 to 60. Data scientist could best determine what the cutoff age for groups based on their biopsychosocial determinants. Each member of the treatment team is only responsible for their area of specialty. However, it is the treatment team practitioner’s responsibility to stay up to date on the latest research regarding their area of specialty and provide updates to the team as needed in a collaborative manner. Any member of the treatment group would have access to any member of the treatment team as the need arise.
The make-up of a treatment team is determined by the members of the treatment group. For example a treatment group of 45 to 55 year old black physically active women would include in addition to the above treatment team an obstetrician.
Treatment group age ranges would be standardized nationally. Best practices for treatment groups would be determined by actual practiced outcomes determined by up to date data collected. Treatment teams would compare notes locally, regionally and nationally. Over time treatment requirement protocols would be determined and updated as data came online.
As a 50YOBM, I want my treatment to reflect who I am not based on outdated norms. Treatment needs to begin segregating populations based on standardized norms from actual data collected. This of course means treatment practitioners need to practice their crafts in real time, collaborating across borders and tracking best practices from colleagues and research scientist. Big Data should be used as a tool in treatment protocols in 21st century care practices.