There is a general set of expectations one has to have about Big Businesses working with Big Data. These are the requirements of for Big Business-Big Data. Businesses that lack this understanding of their population are demonstrating a naivete when it comes to understanding the people they are supposed to be evaluating.
In today’s day and age I find it hard to believe that most businesses have managers on up to CEOs that lack this basic knowledge about their customers, which are of two forms. Their first customers are the clients or other businesses (LOBs) they serve in producing and making use of that LOB data for the benefit of themselves as well as their clients. The second population they are responsible for is that customer population using the LOB and relying on the LOB for improved products, and in the case of improved health care quality of life matched up with lower cost.
Of course, lower cost isn’t ever going to happen. And when an LOB depends upon a third party to improve its income, we have the same product developing that we see with subcontracted governmental work. The more who need payment along the way for a particular process, the more expense that process becomes.
Big businesses that over see how lower lines of businesses operate, the overseers, make for more expensive health care in the long run. A major source of financial support for multiple companies for example is one reason the costs for care have sky-rocketed. Insurance companies that are supported and partially managed by a large revenue source cannot help passing on the costs for that overseer to its members, not its workers or investors, if there are any.
The following simple questions define how a Big Business operates. If your CEO doesn’t know the answer to these questions, you are in a very bad state. If your managers don’t know the answers to these questions, you have to wonder why they are still working. If you personally as a worker know the answers to one or more of these questions, but the upper levels don’t this means there is a serious disconnect within your company.
The questions are as follows:
- What one year age band has the highest number of members for men? for women? (do this for each LOB, or MCD vs MCR vs. Employed)
- What one year age band for members under 18 years of old represents the largest percentage of your population of children? (ditto)
- Obesity, High Blood Pressure or Hypertension (ICDs do differ here), and Diabetes are the two biggest killers and comorbidities for chronic disease management. For HTN and then diabetes, what year (not 5 year range) is the age with the greatest number for each, again for the men? and the women?
- Per gender, which age has the greatest amount of smokers noted in the EMR? How are women different from men with this particular health issue?
- At what one year age does Chronic Liver Disease (all three levels in the ICDs) impact the most males, versus most females?
- At what one year age does osteoporosis skyrocket in costs due to the sudden increase in hip fractures? (contrast one year osteoporosis peak to one year hip-femur fracture peak, again by gender)
- At what one year age does Rheumatoid arthritis start to cost, for each gender?
- At what one year age does non-compliance reach its peak for your membership, by gender?
- At what one year age range does chronic heart disease (let’s say hospitalization for heart attack) level out between the two genders, as men begin to develop claims for this care as much as women?
- What are the age ranges for preventive care, versus palliative care, versus Quality of Life Care, based on CDM and the metabolic syndrome based disorders: Diabetes, HTN and Obesity combined.
- What is the single gender-age in children with the least representation on a population pyramid? (which receives the least care, has the least claims, and at what age?)
- What is the single gender-age in adults with the least representation on a population pyramid?
Now, repeat all of the above questions for Hispanic, African, Latino/a, and Asian communities.
What is meant here are the following observations about the health care industry, with employees more familiar with the total populations than the managers and CEOs spending most of the money are:
- A company with a CEO and management that knows 37 is the cut-off age for high expenses, not 35 or 40, has enough of an understanding to be about the successfully develop highly effective programs.
- Those programs which develop equal quantifiers for anti-smoking programs mailing, support groups etc. are wasting money on one of the two genders.
- Those programs which don’t know the relationship between cost per ICD/V-/E-code, for what gender-age, are unable to quantify how much they saves in lives or money when it comes to providing high quality, cost effecive health care.
The above pertains just with the population health level.
These same questions can be evaluated nationwide in small areas, per county or less. In other words, one can learn where the most resistant to health care advances and disease prevention are, where the peak populations are for refusing immunizations or non engaging in well visits, where costs generated are costing the company the most, and with what program, involving which socioeconomic settings.
With population grid mapping, the single area with the most child or spouse abuse can be isolated, the most abusive parents can be located, the most non-compliant mid-age patients can be discovered, the most fraudulent, abusive drug refill/use areas can be identified. We can better target our costs for interventions using this approach.
The results of NPHG is really the data that management and CEOs/CIOs need to know, not the results of the BI approach that is now normally taken.