Let’s consider a single security trader.
All they trade is IBM. All they need to know is that security and its included indexes. But start trading another security, such as Cisco (CSCO), while they have a position in IBM, and they have a portfolio. Portfolios behave differently – profiting or losing on an aggregate basis from the combination of movements in multiple securities. For example, if you hold 10,000 shares of IBM and CSCO, and IBM appreciates by a dollar while CSCO loses a dollar, you have no net gain or loss. That’s called portfolio risk.
Everything in the markets is connected. For example, if you’re an institutional trader, with a large (1,000,000 shares +) position in IBM, you know that you can’t sell quickly without tanking the market. That’s called execution risk. Also, once the US market closes (less of a concern these days than 20 years ago) there is less liquidity. Imagine you are this large institutional trader, at home at 11pm. A breaking news story develops about a train derailment of toxic chemicals near IBM’s research campus causing fires. You suspect that it destroyed all of their most prized experimental hardware which will take years to replace. Immediately, you know that you have to get out of as much IBM as possible to limit your losses. However, when you get over to your trading terminal, the first bid in the market is $50 lower than the price that afternoon for a minuscule 10,000 shares. If you sell at that price, the next price will be even lower for a smaller amount. You’re stuck. However, there is a relationship between IBM and the general market called a beta which is a correlation coefficient. Since you cannot get out of your IBM directly, you sell a defined number of short S&P futures in the open market to simulate a short position in IBM. You’re going to take a bath, but not as bad as the folks that went to bed early and didn’t react to the news.
A sufficiently large portfolio with >250 stocks will approximate broader market indexes (such as the S&P 500 or Russell index) depending upon composition. It’s beta will be in the 0.9-1.1 range with 1.0 equaling a perfect correlation coefficient (r). Traders attempt to improve upon this expected rate of return by strategic buys and sells of the portfolio components. Any extra return above the expected rate of return of the underlying is alpha. Alpha is what you pay managers for instead of just purchasing the Vanguard S&P 500 index and forgetting about it. It’s said that most managers underperform the market indexes. A discussion of Modern Portfolio Theory is beyond the scope of this blog, but you can go here for more.
So, excepting an astute manager delivering alpha (or an undiversified portfolio), the larger & more diversified the portfolio is the more it behaves like an index and the less dependent it is upon the behavior of any individual security. Also, without knowing the exact composition of the portfolio and it’s proportions, it’s overall behavior can be pretty opaque.
MAIN POINT: The portfolio behaves as it’s own process; the sum of the interactions of its constituents.
I postulate that the complex system of healthcare delivery behaves like a multiple security portfolio. It is large, complex, and without a clear understanding of its constituent processes, potentially opaque. The individual components of care delivery summate together to form an overall process of care delivery. The over-arching hospital, outpatient, office care delivery process is a derivative process – integrating multiple underlying sub-processes.
We trace, review, and document these sub-processes to better understand them. Once understood, metrics can be established and process improvement tools applied. The PI team is called in, and a LEAN/Six Sigma analysis performed. Six sigma process analytics typically focus on one sub-process at a time to improve its efficiency. Improving a sub-process’ efficiency is a laudable & worthwhile goal which can result in cost savings, better care outcomes, and reduced healthcare prices. However, there is also the potential for Merton’s ‘unintended consequences‘.
Most importantly, the results of the six sigma PI need to be understood in the context of the overall enterprise – the larger complex system. Optimizing the sub-process when causing a bottleneck in the larger enterprise process is not progress!
This is because a choice of the wrong metric or overzealous overfitting may, while improving the individual process, create a perturbation in the system (a ‘bottleneck’) the negative effects of which are, confoundingly, more problematic than the fix. Everyone thinks that they are doing a great job, but things get worse, and senior management demands an explanation. Thereafter, a lot of finger pointing occurs. These effects are due to dependent variables or feedback loops that exist in the system’s process. Close monitoring of the overall process will help in identifying unintended consequences of process changes. I suspect most senior management folks will recall the time when an overzealous cost-cutting manager decreased in-house transport to the point where equipment idled and LOS increased. I.E. The .005% saved by patient transport re-org cost the overall institution 2-3% until the problem was fixed.
There is a difference between true process improvement and goosing the numbers. I’ve written a bit about this in real vs. fake productivity and my post about cost shifting. I strongly believe it is incumbent upon senior management to monitor middle management & prevent these outcomes. Well thought out metrics and clear missions and directives can help. Specifically – senior management needs to be aware that optimization of sub-processes exists in the setting of the larger overall process and that optimization must also optimize the overall care process (the derivative process) as well. An initiative that fails to meet both the local and global goals is a failed initiative!
It’s the old leaky pipe analogy – put a band-aid on the pipe to contain one leak, and the increased pressure in the pipe causes the pipe to burst somewhere else, necessitating another band-aid. You can’t patch the pipe enough – too old. The whole pipe needs replacement. And the sum of repairs over time exceeds the cost of simply replacing it.
I’m not saying that process improvement is useless – far from it, it is necessary to optimize efficiency and reduce waste to survive in our less-than-forgiving healthcare business environment. However, consideration of the ‘big picture’ is essential – which can be mathematically modeled. The utility of modeling is to gain an understanding of how the overall complex process responds to changes – to avoid unintended consequences of system perturbation.