Obamacare: watch out, here comes “predictive modeling”
By Jon Rappoport
June 5, 2014
Modeling or programming?
A Medai.com article, “Why is Predictive Modeling Essential to Healthcare?” offers this quote:
“…the algorithms of predictive modeling can analyze hundreds of data points to make a diagnosis or a prediction of risk.”
This is the new thing in American medicine, and everyone is climbing onboard. The idea is to combine diverse sets of data, to diagnose a patient—and also predict what illnesses will strike designated sectors of the population by identifying what social, economic, gender, and behavioral groups they belong to.
“Oh, yes, in USA population sector A-2ab, we can view the electronic health records of 10,000 patients who are single, under 30, live at home with their parents, have a history of ignoring medical advice, display symptoms of ADHD, graduated college with less than a 3.0 grade average, have taken prescription pain meds within the last five years…according to statistics, this group stands a better than 65% chance of developing clinical depression within the next 6.23 years. Therefore, we should prescribe them prophylactic antidepressants now, to save money on more expensive treatments later. If we utilize our algorithm and adjust code 4aQ1 and code 7B2Ex, we’ll be able to pinpoint which patients in this group need medication immediately…”
This is coming. The structure is being built now, across all medical and insurance organizations.
The hubris involved is outstanding, to say nothing of the intrusion on privacy, and the recent abysmal track record of government merely trying to sign people up for Obamacare.
Of course, the proponents of prediction are promising better patient outcomes, enormous $$ savings, and a Brave New World in which “at-risk” groups can be spared suffering before it occurs.
Managed Care has published an article, “More Data in Health Care Will Enable Predictive Modeling Outcomes.”
Here are two key quotes:
“Predictive modeling (PM) has grown to be a linchpin of care management. Health plans, integrated delivery systems, and other health care organizations (HCOs) increasingly channel their patients to interventions based in part on what they deduce from predictive models that have traditionally been run against databases of administrative claims. In this arena, the Affordable Care Act (ACA) [Obamacare] is likely to exert a profound effect.”
“…a growing number of health care experts, including the Care Continuum Alliance, see predictive modeling as an opportunity to prevent [disease] complications, control [hospital] readmissions, generate more precise diagnoses and treatments, predict risk, and control costs for a more diverse array of population segments than previously attempted.”
I could attack this gobbledegook PR in a number of ways, but I’ll cut to the real bottom line. The entirety of predictive modeling rests on the assumption that, in making disease and mental-disorder diagnoses, doctors are basically working from a correct playbook. Otherwise, they could massage data ’til the cows come home and they would commit disastrous blunder after blunder.
But the basic playbook is not correct. For all 300 officially certified mental disorders, there are, in fact, no defining physical tests for diagnosis. None. No blood tests, no urine tests, no brain scans, no genetic assays.
For germ-caused diseases, the “tried and true” diagnostic tests are completely misleading. The antibody tests are frequently false-positive, and the presence of antibodies, in the first place, merely indicates that the patient has contacted the germ in question. It says nothing about present or future illness. In fact, a positive antibody test often means the opposite of illness: the patient’s immune system has contacted the germ and thrown it off.
The PCR diagnostic test takes a tiny, tiny amount of what is purported to be genetic material from a germ, and amplifies it many times so it can be observed. But the hallmark of illness, when germs may be involved at all, is: you have millions of a particular bacteria or virus being very active—in which case, you didn’t need the PCR test in the first place. You needed the PCR because you couldn’t find enough germ material in the patient to knock over a fly.
So…place these irrelevant and misleading diagnostic tests into the predictive modeling framework, and what do you get? False predictions.
Then there are the medical drugs. I’ve cited the Starfield Review many times. July 26, 2000, Journal of the American Medical Association, “Is US Health Really the Best in the World?” Dr. Barbara Starfield, from the Johns Hopkins of School of Public Health, pointed out that, every year in the US, FDA approved medicines kill 106,000 people. In our 2009 interview, Starfield told me this was a conservative estimate.
This is tragedy and murder on a massive scale, supported by what have to be deceitfully glowing studies done on the drugs and routinely published in leading medical journals.
No one in his right mind would believe that all this lying and crime, embedded in a new and advanced predictive model, would produce anything except expanded disaster.
With the bonus that you lose your medical privacy, and you are observed and data-mined in other ways—as a “unit” that belongs to various groups. And oh yes, you get toxic drugs before you’re sick. It’s predictive. Hail to the Chief.
Up the line, proponents of traditional/ natural health, nutritional supplements, and “alternative practices” will discover that, as independent humans, they’re anomalies. The official algorithms don’t include what they do to maintain health. Therefore, they’re outliers.
“Sorry, we can’t model you in our predictions. You don’t compute. You’re illegal. Oh, wait. You do fit into one category: Threat. Potential Danger to the State.”