Can Algorithms Find the Best Intelligence Analysts? – Not Until Biases and Memory Lapses Are Overcome And That May Be A Long Time Coming And That Goes for Health Insurance Analytics Too

In the race for data analysis, it still seems that even with aggregating and taking the best of the best, we still need those decision making processes from humans and the fact that mathematics is not improving this may come as a shock to some, those addicted to the algorithmic analysis processes.

However, they are still trying to make a “go” of it and the intelligence community is holding a one day workshop to try and figure out how to pursue this. High stress situations are the weak links here, so what’s not stressful any more.

The biggest issue here is human “memory lapses” to deal with, and we all have those, except when it comes to creating out healthcare history, insurance carriers don’t seem to want to take that into consideration as “they have data”.

There are a lot of folks out there that think the algorithms are the only answer and they are not; however when it comes to claim information and underwriting, oh boy they are everything. Ask any analyst that works for an insurance company and even Wendell Potter told us that, but we still battle that paradigm. Morality can’t be left out of the picture.

What those algorithms accomplish for health insurance companies though is make money. When human imperfections arise, well guess what we have to find blame somewhere and it comes back to the patient with digging around through records, etc. to cut risk and when that happens they are in essence wanting to eliminate human imperfections, and thus there goes morality down the toilet. We are only as strong as the weakest link as this bunch discovered too when pooling the top analysts, it didn’t make any difference on that end, but they are not making a profit at it either.

One other common items here too is that the article says it will tell the experts what they are worth, one more paradigm to how health insurance is run when we are “scored” all the time. “Expert judgments may be seriously flawed, but often are the only game in town”, and boy do we not know that one as we do not get to see the analysis process or the algorithms in process, but again besides Medicare, the carriers are the only game in town with the data.

Until the intelligence organization Larpa has figured out a way to eliminate biases and memory lapses, there’s no perfect algorithm to predict and forecast and again perhaps this understanding could help insurance carriers to be aware of this fact when working with claims and underwriting. If even the experts can’t figure this one out, how can we as patients be held to such high expectations with our memory and documentation with our health history. BD

The U.S intelligence community has a long history of blowing big calls — the fall of the Berlin Wall, Saddam’s WMD, 9/11. But in each collective fail, there were individual analysts who got it right. Now, the spy agencies want a better way to sort the accurate from the unsound, by applying principles of mathematics to weigh and rank the input of different experts.

Iarpa, the intelligence community’s way-out research arm, will host a one-day workshop on a new program, called Aggregative Contingent Estimation (ACE). The initiative follows Iarpa’s recent announcement of plans to create a computational model that can enhance human hypotheses and predictions, by catching inevitable biases and accounting for selective memory and stress.

ACE won’t replace flesh-and-blood experts — it’ll just let ‘em know what they’re worth. The intelligence community often relies on small teams of experts to evaluate situations, and then make forecasts and recommendations. But a team is only as strong as its weakest link, and Iarpa wants to fortify team-based outputs, by using mathematical aggregation to “elicit, weigh, and combine the judgments of many intelligence analysts.”

But until Iarpa’s also mastered their plan to nip biases and memory lapses, they’ll still be forced to contend with the inevitability of human imperfection.