The length of the pipe reflects a number of factors that determine the time it takes to get treated appropriately: time to get an appointment, how long it takes to figure out what's wrong, time to get tests and referrals, time to see if the treatment is working, etc. This pipeline serves a diverse population. As in the figure below, we can roughly divide the population into 4 categories. The first is the really healthy people - I left them off. The other 3 groups are the people trying to get seen (black), those who can’t afford to be seen (the ones in blue) and the ones who SHOULD be seen but don’t know it (the greenies). Generally, more people are trying to get seen that can be accommodated at a given time.
Why should the greenies be seen? Because the greenies may have an occult condition and not know it: hypertension, heart disease, pre-diabetes, undiagnosed cancer, etc. Or they may be at risk for some ailment(s) that could be prevented if they did the right things. The problem is that it’s hard to get people to do anything when they are feeling ok and the clinicians are already so busy taking care of obviously sick people that there is no time to do much prevention.
Why it Matters
It matters because if the condition is serious, it's a race against the clock. Keep in mind that we are already behind the eight ball because of what’s in the diagram below. Currently, a lot of time has passed from when something starts till when we notice it enough to actually call the doctor in the first place. Then it takes time to do all those things we talked about before - and that assumes nobody makes a mistake or takes extra time.
The importance of the time it takes to decide to do something and for the doc to figure out what’s wrong is reflected in next this graph.
On average, the longer it takes to get something fixed, the worse is the outcome and the higher the cost. It’s really that simple.
So if we want to really reform health care we need to shorten this timeline as much as possible. Take a look at the impact of cost and outcome to a shorter timeline.
If we can accomplish this task of compressing clinical timelines over the course of millions of lives the impact is staggering. Estimates of the cost reductions alone range from 25%- 50% of current levels! Or somewhere between $ 500 billion and $1 trillion dollars EACH YEAR! And that is just in the healthcare costs not all the societal costs associated with disease such as lost work, etc.
It makes sense but how do we do this? Well first off, you don’t just play with the payment structure or throw money at the problem (the traditional single payer approach)
Paying people less to do more doesn’t open that pipe up much. You still have people who can’t get in to the clinic and people who don’t know they need any help, still don't. Single payer may be part of the answer but it isn’t THE answer. Plus concentrating just on the costs results in some really dumb ideas - like taxing clinicians to take care of patients.
Nor can you just build a bigger pipe. Training more docs and mid levels for example takes time and costs a lot of money. Which leaves no money left to care for those without coverage. This is one of the approaches that the professional societies advocate - train more experts. The bottom line is that we can’t afford it.
Plus the bigger pipe may reduce some of the time spent waiting for an office visit but does nothing to reduce the length of the pipe itself - how long it takes to correctly get to the bottom of the problem. More doctors does not necessarily result in better decision-making by any of them.
Some compression of the timeline can be achieved using diagnostic support software. By using decision-support tools to aid diagnosis, we can in essence cut out some of the length of the pipe reducing the time it takes to get the right answer. But that alone, cannot increase the size of the pipe and the rate at which patients can be seen to the levels we need to achieve universal access.
Combinations of these approaches still fall short in some key ways. We still are left with a system we can't afford, that can't serve the whole population.
But the solution, may be in the cloud...
Next time - a cloud computing solution to health care reform.