A few years back I was talking to a Forensic Analytics company Beyondcore about why the Affordable Health Care Act wasn’t affordable. Forensic Analytics is a very specialized field which looks and the core reasons for why something is. We tend to spend a lot of time on the what, in this case Obama Care sucks, but not nearly enough of the why which speaks to the difficulty in fixing it. I mean if you don’t understand why something is broken you’ll likely fail at fixing it which is clearly a big part of the mess in getting the replacement for Obama Care through congress, it actually looks worse and no one really wants worse.
Let’s talk about the core problem with the national healthcare effort then and now and why an information/analytics approach to the problem could come up with a better solution. However, this also points to the common problem with analytics, often people leave off a critical factor and end up making a bad decision. Which I think speaks to the train wreck we are now seeing.
The core problem that Beyondcore was trying to address was the fact that when proposed the Obamacare numbers looked pretty good and the core numbers had been deeply analyzed by a variety of organizations and analysts. But, once in place, the red ink poured out of the program causing some smart politicians and bureaucrats to ask why. As it turns out there was a fundamental mistake made in the analysis. A core belief in the program was that young users, who would be required to buy into the program, would generate enough extra cash to pay for the lower health and higher healthcare usage of older users. This was based on the accurate belief that young users don’t go to doctors as often, but what was left out of this analysis was psychiatric care which was covered by the program. Apparently, Millennials need an unusual amount of psychiatric care which wasn’t factored in when the program was created and thus generate little or no extra cash so the medical needs of the older users weren’t in fact covered. This Obamacare, because psychiatric care was left out of the analysis but covered by the insurance, tanked the financials of the program.
Now another problem with this concept was that the program was intended to cover pre-existing medical problems from those that didn’t have insurance. This isn’t insurance it is basically a gift from the State to those that either chose not to carry insurance or couldn’t afford it. This cost had to be spread to those that had paid for insurance as an effective tax concealed as an insurance rate increase and/or a reduction in benefits. Many of us got both, our rates went up along with our co-pays and costs associated with medication and special procedures.
When a tax is hidden like this it doesn’t appear like a tax and people can’t effectively object to it but the dissatisfaction with the program speaks to the fact that just concealing a problem like this doesn’t get rid of it.
However, the far larger problem is that the United States ranks toward the top in terms of per-capita medical expenses but near the bottom in terms of quality of coverage. It is very much like we are paying for brand new Rolls Royce quality service but actually getting used Yugo, which isn’t sustainable. If you have a cost vs. service problem you need to address this first but instead both parties are largely focused with moving these unaffordable costs around to different groups of people who understandably don’t want to pay them. If either party truly wanted this to work and assure they would be reelected a focus on eliminating these extreme costs would seem to be the more intelligent path but, instead, both groups seem to be focused on sticking these costs to the demographic that typically votes for the rival group.
At the heart of this problem is the seeming belief that Universal Healthcare is a weapon to be used against rival politicians rather than a necessary benefit to assure the health of the nation.
The United States is the world leader in technology and certainly in the lead with regard to analytics and artificial intelligence. These tools can be incredibly effective at helping critical decision makers make ever more intelligent decisions. However, they don’t protect against stupid mistakes like intentionally leaving out critical information like psychiatric needs of young people, or avoiding the core problem that needs fixing like the fact healthcare costs in the US are unusually unaffordable.
This is partially why a lot of us are concerned that the coming Artificial Intelligence wave may wipe us out. Because people are training this first generation of AIs which will be able to think and act far more quickly than and if they are equally flawed that means the mistakes that are coming will come faster in the future. A lot of us aren’t sure we can survive a massive increase in the number of mistakes coming out of the government. It’d would be really bad if the Terminator, rather than being a worst-case scenario, turned out to be a better alternative than what we actually end up with.