Medical systems in three phrases.

Finding things wrong with the US medical system is like dynamiting fish in a barrel.

Finding ideas for improvements isn’t any harder.

Implementing such ideas or even simply validating whether they are good ideas is much, much harder.

But one day I stood back and considered the “system” while keeping in mind three little phrases.

1) Who cares? It’s not my money.

2) You get what you pay for.

3) First, do no harm.

There may be other phrases as pithy and relevant. I don’t know. Can you think of any?


Let’s flesh these phrases out:

Who cares? It’s not my money.

Medical expenses are disconnected between payer and payee. Given regulatory realities, if you want to control your own medical expenses, you need a competing system – in another country.

But, going to another country is not often an option. US medical expenses are dominated by Medicare/Medicaid. Medicare/Medicaid don’t pay foreign medical bills.

When you are insulated from the price of medical care, your are not the customer. You are the product, perhaps. The raw material, perhaps. But you are not the customer.

Imagine buying something from Mr. Someone without knowing the price until your bank account has been debited for that Wells Fargo money order you sent to Mr. Someone. Ah, you would be the “mark”, perhaps, but not the customer.

You get what you pay for.

We all deeply know that “cheap” is cheap and “expensive” is high quality.

When you are sick or broken, do you want a cheap fix? Gosh no! You want the best money can buy. Since you have no clue what particular fix you want, it’s safest to go with the expensive fix and hope for the best.

Just try to justify a cheap fix for someone else’s body. Don’t you look horrid? Yes, you do, you uncaring cheapskate.

So, the existing medical system is a cost maximizing system. By demand.

First, do no harm.

Medical practice is not perfect. Many diseases and other negative attributes of our bodies are not dealt with well at all. This will always be true.

So how does the “system” find cures or fixes?

Carefully. By “hill climbing”.

“Hill climbing” is a simple, universal search method. When hill climbing, you start from where you are and look around your neighborhood for a better place to be. You go to that place and do the same thing again. And again. And again. Until you find yourself in the best place in your neighborhood. You have found what you are looking for. Search complete.

For example, imagine looking for a cure for cancer.

You have a current therapy for cancer. But is there a better one?

Well, you *could* search for one by randomly trying all sorts of things:

* Homeopathic beets.

* Up-beat music.

* Vegetarian fish.

* And so on.

But, “First, do no harm.” Ignoring the current, best therapy can certainly qualify as doing harm. So, to find a better therapy, you modify the current, best therapy by just a very little. Usually, you add something to the current best therapy – an extra “medicine”. Just enough to check a similar, nearby therapy. Carefully. Then, if this new therapy is an improvement, you switch to it, and do the process again. Carefully.

As a strategy, hill climbing can work very well. Unless the possibilities are vast or the best therapies don’t have wide, easily found slopes leading up to them.

Hill climbing gets stuck on what are called “local maxima” – the best place in the vicinity. Not the best place. Only the best place near the searcher’s current location.

Hill climbing is not a good way to find breakthroughs. Breakthroughs happen when someone gives up on current practice and flies off on a tangent. Doing harm.

Consider ants when they know their food source. They file to and fro, slightly improving the path to the source by cutting corners until the path is short and easy. They do no harm.

When the path is broken, the ants wander around in a peculiar random way, casting about for some indication of food.

They can die wandering randomly. “Tough break, Mr. Ant. Hard times call for hard measures. You do yourself harm for the greater good.

Uh, huh. Sell that to Hippocrates and his oath.


So there you have it. Food for thought.

Steep Speedy Hikes

It started as curiosity. What do GPS tracks say about the ratio of uphill and downhill hiking speeds?

It became graphs of all the hikes I’ve done since ’07 showing speed against the hike’s angle of slope.

Here are the average absolute slope angles on a per-track basis. If the dot’s high up, the hike was on a steep hill.

All hike speeds by track.

Yes, those high tracks before 2012 were steep. Mailbox Peak, Guye Peak, and Wagonwheel Lake, for example. Good stuff. A week after wobbling to the car below Wagonwheel Lake, I was merrily springing up the stairs at home.

Notice the laid back hiking in ’12 and ’13. … Sigh. … 2012 was a lost summer – lost working too much while the sun shone outside. The 2013 hiking season was spent in chemo-land. The cluster of flat hikes at the end of 2013 was me getting strength back by looping Maplewood.

Here are the average track speeds. It shows Scott’s bike a few times in the last couple years. The cluster of 5 kph tracks at the end of 2013 are the flat, Maplewood strolls mentioned above.

All hike slopes by track.

One of the slow speed hikes in late 2014 was up Adams Peak (Sri Pada) in Sri Lanka. Here are how the point-speeds on that walk distribute as a function of slope angle.

Sri Pada speeds by slope.

That hike’s graph really shows the difference between down and up-hill speeds. I “ran” down a lot, but you don’t enthusiastically race straight down 5000 concrete stairs.

Here’s the same sort of thing for Wagonwheel Lake:

Wagonwheel Lake speeds by slope.

As noted below, GPS points are noisy, any way you spin ’em. But the overall fit is OK.

Here are all the tracks’ points graphed as a function of slope.

All hike speeds by slope.

Bike ride speeds tower above the others. The near-level-ground points in the middle of the graph are, in fact, skewed to the left – downhill – to negative slope angles. They don’t look so in this graph for tech reasons.

Finally, here is a PDF containing scalable versions of the all-hike graphs above.

all_hikes.pdf

Note: These graphs were made from “hikified” GPS tracks. Points in a line between two points are eliminated by the “hikify” logic. That logic also combines GPS points near each other. But, even at a filtered, combined point scale, GPS data is noisy.

Python 2.7 scripts in the usual state of repair:

hikify.zip
gps_hill_speed_plot.zip