The last word on variations

“ Facing an onslaught of regulatory changes and major market pressures, health care providers are grappling with how to transform existing services as part of a push to raise the quality of patient care while boosting efficiencies and reducing costs.”—MicroSoft

Significant recent press has spoken to the issue of variations in costs. On the one hand, health care costs vary significantly from geographic region to region and from hospital to hospital. Some would use this information to indict the system as lacking in control or possibly gouging where the market will bear higher prices.

Just last week, the Colorado Division of Insurance released its findings of wide variations in health premiums to be sold through the Exchange. Those on the Exchange Pom-Pom team cited this as evidence of competition and choice for consumers. What do we really make of a situation whereby health care providers exhibit high variations in costs which we proclaim as bad but we heap praise on the financial intermediaries who arbitrage those same services and add their own measure of profit.  I maintain there is both a devil dressed as an angel in drag here.  You have to drill down and look at the detailed picture.

When analyzing and making decisions, most of us calculate average costs to have a working number. To be precise, a health policy professional (like the person who regularly follows The Thought Czar) will not merely use crude averages, but will display the underlying data as a bell-shaped curve, aka, a frequency distribution curve. Without jumping into the briar patch, let me point out that a knowledge of the shape and distribution of the underlying data is important before we reach any conclusions.

                        BellCurve

 

Variations in outcomes, of which “cost” is paramount, are an outgrowth of both natural and human managed systems. This phenomena in health care has been well known ever since Jack Wennberg and Phil Caper created the Dartmouth Health Atlas back in the 1980s.

Let’s assume for the moment that you are the “Big Data Analyst” at the NSA of Healthcare, operating out of your bunker in Nevada.   What would you do with this data? What conclusions can you reach? Those suspicious of market player motives might view “high price chargers” as taking an opportunity to overcharge whenever possible. Others might see “low cost chargers” as competitors striving to outdo their rivals

I look at it differently. If you take the distribution of prices(variations) in the health care market you can only make rational decisions if you know quality. Think for a moment, the difference in prices listed on Craigslist for two similarly looking autos can only be used as a basis for purchase if you know that one is a Mercedes and the other a Kia. By what means could we sort through the complexity of costs and quality differences?

Below is a frequency distribution curve embedded in a classic 2×2 matrix. In  the upper right-hand quadrant, there are high quality/high price providers(insurers) and in the lower, left-hand quadrant are low cost/low quality providers(insurers). You might have to meditate on the graphic to understand it fully, but it is one way to frame out the problem and solution to the cost/quality conundrum.

Reimbursement Model_revised

In viewing data in this way, we also can reach an even more profound set of conclusions. Let’s suppose you are the federal government or a big insurance company. You use your bargaining clout to come in and impose a fee schedule on providers. If you are the 800lb gorilla, like Medicare you will demand prices far below average so that you can prove to the electorate that you are their loyal agent in getting them a good deal. And, if you are the big-dog-on-the-porch insurance company (essentially one of the seven largest who are oligarchs) you will attempt to peg your fee schedules to a shadow of Medicare.

Now, you might promise the provider more patients, but there are few incentives other than copay or deductible differentials to allow you to make that happen. And, you will likely have promised the employer or individual lower premiums. The reality, though is that the Division of Insurance data for the past five years has shown “exclusive-provider- networks” are more expensive than “open-networks”.

For the time being, go back to the frequency distribution(variations) curve and climb it like a mountain. At price P.5., which is in the low cost/low quality quadrant, paying the average price would still overpay the quite a few providers because they are vending poor quality. But, paying the higher “average” price also conspires to penalize high quality providers and it may not pay their costs. So, quality is driven from the market(Law of Lemons), leaving low quality providers willing to accept low prices. Similarly, paying price P.4, which is an above average price may over-reward low quality providers and  still bears the risk of underpaying the highest quality provider.

If your eyes have glazed over by now and I am at risk of losing you, step back from the graphic. What does all this boil down to?

  1. All systems exhibit variations and there can be both innocent or diabolical  reasons for this occurring.
  2. Paying  a simple average price rarely takes into account the quality differences. It risks making an irrational decision.
  3. Merely thinking you are getting a good deal because you are paying less than average risks penalizing providers who should be rewarded. It almost always overpays the scoundrels.
  4. Over time, negotiated fee schedules distort the market. They drive out high quality because they punish efficient provider.  Individuals with high standards leave the market and it must subsist on expensive lemons.
  5. Financial intermediaries arbitrage by exploiting differences and normally play a parasitic role.

 

Prior to the Omnibus Reconciliation Act of 1983, most health care was paid based on costs. It was required to get providers to accept Medicare. In hindsight it was a recipe for hyper-inflation. But, attempting to chill inflation by implementing fixed-fee schedules under prospective payment lead to an even worse cascade of unintended consequences.  The cumulative effect of fifty years of ill-conceived public policy initiatives, when taken as a whole, has rendered the health care market a brownfield. In the end, the self-pay patient has been left abandoned and in need of rescue, like a wounded soldier on the battlefield. That class of patients has had no negotiating leverage and has had to play the game in a foggy market where prices and quality are impossible to determine. As a result, the self pay patient, who we normally rely on to eliminate dead weight loss, has ended up paying rack room rates while government and corporate payors extracted up to 70% discounts from the system. Now that we wish to increase access to the 50 million people without insurance, we find ourselves painted into a corner. The system has become so inflated that our last ditch effort is to force young and healthy patients into a risk pool and use their lower costs to subsidize older, sicker patients. Like bubble gum, it will soon lose its taste. It is unsustainable and will eventually cease to function whether policy makers do anything about it or not.

 

 

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